predictions_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ad757fc",
   "metadata": {},
   "source": [
    "# Trained Smart Nanogrid Model Predictor with Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b84492f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "import os\n",
    "import argparse\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots, go\n",
    "import plotly.io as pio\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "import json\n",
    "\n",
    "from stable_baselines3 import PPO\n",
    "from smart_nanogrid_gym.utils.config import solvers_files_directory_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f4638d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.plotting.backend = \"plotly\"\n",
    "pio.templates.default = \"plotly_white\"\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b361c25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_single_day(current_model, env, kwargs):\n",
    "    rewards_list = []\n",
    "    actions_list = []\n",
    "    \n",
    "\n",
    "    obs, _ = env.reset(**kwargs)\n",
    "    done = False\n",
    "    while not done:\n",
    "        action, _states = current_model.predict(obs)\n",
    "        obs, reward, terminated, truncated, info = env.step(action)\n",
    "        done = terminated or truncated\n",
    "        rewards_list.append(reward)\n",
    "        actions_list.append(action)\n",
    "\n",
    "    return rewards_list, actions_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff0dad74",
   "metadata": {},
   "outputs": [],
   "source": [
    "environment_mode = 'prediction'\n",
    "# Modes: 'no_penalty', 'on_departure', 'sparse', 'dense'\n",
    "penalty_mode = 'sparse'\n",
    "# Variants: \n",
    "#    -'basic'(only vehicle charging from the grid)\n",
    "#    -'b-pv'(vehicle charging alongside grid, solar energy from the PV system and dis/charging battery system)\n",
    "model_variant = 'b-pv'\n",
    "\n",
    "charging_mode = 'bounded'\n",
    "number_of_chargers = 4\n",
    "\n",
    "# Time intervals: '15min'(0.25), '30min'(0.5), '45min'(0.75), '1h'(1), '2h'(2)\n",
    "time_interval_string = '1h'\n",
    "time_interval = 1\n",
    "\n",
    "agent_algorithm = 'PPO'\n",
    "timesteps_per_episode = 24 / time_interval\n",
    "\n",
    "prediction_episodes = 1\n",
    "prediction_steps = timesteps_per_episode\n",
    "\n",
    "device = 'cuda' if number_of_chargers >= 8 else 'cpu'\n",
    "\n",
    "data_directory = 'single_prediction_files'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05abb920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PPO-b-pv-bounded-sparse-4ch-1h'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = f'{agent_algorithm}-{model_variant}-{charging_mode}-{penalty_mode}-{number_of_chargers}ch-{time_interval_string}'\n",
    "file_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91551e9d",
   "metadata": {},
   "source": [
    "JSON file containing INITIAL model values used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1f4b94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'solvers/RL/single_prediction_files/PPO-b-pv-bounded-sparse-4ch-1h-initial_values.json'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_values_path = f'solvers/RL/{data_directory}/{file_name}-initial_values.json'\n",
    "initial_values_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c03502",
   "metadata": {},
   "source": [
    "JSON file containing PREDICTED model values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "409e28a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'solvers/RL/single_prediction_files/PPO-b-pv-bounded-sparse-4ch-1h-prediction_results.json'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_results_path = f'solvers/RL/{data_directory}/{file_name}-prediction_results.json'\n",
    "prediction_results_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1d65bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "env_variants = [\n",
    "    {\n",
    "        'variant_name': 'basic',\n",
    "        'config': {\n",
    "            'vehicle_to_everything': False,\n",
    "            'pv_system_available_in_model': False,\n",
    "            'battery_system_available_in_model': False,\n",
    "            'environment_mode': 'training',\n",
    "            'algorithm_used': 'PPO',\n",
    "            'number_of_chargers': number_of_chargers,\n",
    "            'charging_mode': charging_mode,\n",
    "            'vehicle_uncharged_penalty_mode': penalty_mode,\n",
    "            'time_interval': time_interval_string\n",
    "        }},\n",
    "    {\n",
    "        'variant_name': 'b-pv',\n",
    "        'config': {\n",
    "            'vehicle_to_everything': False,\n",
    "            'pv_system_available_in_model': True,\n",
    "            'battery_system_available_in_model': True,\n",
    "            'environment_mode': 'training',\n",
    "            'algorithm_used': 'PPO',\n",
    "            'number_of_chargers': number_of_chargers,\n",
    "            'charging_mode': charging_mode,\n",
    "            'vehicle_uncharged_penalty_mode': penalty_mode,\n",
    "            'time_interval': time_interval_string\n",
    "        }}\n",
    "]\n",
    "\n",
    "current_env = env_variants[1]\n",
    "current_env_name = current_env['variant_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3402880b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'vehicle_to_everything': False,\n",
       " 'pv_system_available_in_model': True,\n",
       " 'battery_system_available_in_model': True,\n",
       " 'environment_mode': 'training',\n",
       " 'algorithm_used': 'PPO',\n",
       " 'number_of_chargers': 4,\n",
       " 'charging_mode': 'bounded',\n",
       " 'vehicle_uncharged_penalty_mode': 'sparse',\n",
       " 'time_interval': '1h'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_env_configuration = current_env['config']\n",
    "current_env_configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1f482c",
   "metadata": {},
   "source": [
    "## ! Za vizualizaciju posljednjeg predviđanja za određeni tip modela (ukoliko postoji) - Preskočiti retke ispod do kategorije \"MODEL PREDICTIONS VISUALISATION\" i od tamo nastaviti pokretati kod !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4f6f1eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('SmartNanogridEnv-v0', **current_env_configuration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a7014a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Development\\\\PyCharmProjects\\\\smart-nanogrid-gym\\\\solvers\\\\\\\\RL\\\\models\\\\PPO-b-pv-bounded-sparse-4ch-1h'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dir = f\"{solvers_files_directory_path}\\\\RL\\\\models\\\\{file_name}\"\n",
    "model_path = f\"{model_dir}\\\\999600\"\n",
    "model_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "21a37294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b-pv'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_env_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2ab6c583",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n"
     ]
    }
   ],
   "source": [
    "uppercase_name = file_name.upper()\n",
    "new_model = None\n",
    "model = None\n",
    "if 'DDPG' in uppercase_name:\n",
    "    new_model = DDPG.load(model_path, env=env, device=device)\n",
    "    model = {'name': file_name, 'model': new_model, 'env_name': current_env_name, 'info': {'algorithm': 'DDPG'}}\n",
    "elif 'PPO' in uppercase_name:\n",
    "    new_model = PPO.load(model_path, env=env, device=device)\n",
    "    model = {'name': file_name, 'model': new_model, 'env_name': current_env_name, 'info': {'algorithm': 'PPO'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "67cc5ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rewards = {}\n",
    "mean_rewards = {}\n",
    "final_rewards[file_name] = 0\n",
    "mean_rewards[file_name] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7cabaa00",
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_config = {'generate_new_initial_values': True}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6567b6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Development\\PyCharmProjects\\smart-nanogrid-gym\\venv\\lib\\site-packages\\gym\\utils\\passive_env_checker.py:233: DeprecationWarning:\n",
      "\n",
      "`np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reset_config['algorithm_used'] = model['info']['algorithm']\n",
    "reset_config['environment_mode'] = 'prediction'\n",
    "\n",
    "rewards, actions = predict_single_day(model['model'], env, reset_config)\n",
    "\n",
    "model_name = model['name']\n",
    "final_rewards[model_name] = sum(rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0843250b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_rewards[file_name] = np.mean(final_rewards[file_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b372be8",
   "metadata": {},
   "source": [
    "### MODEL PREDICTIONS VISUALISATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e091e2b6",
   "metadata": {},
   "source": [
    "Initial values used in prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "79287fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_tariff_high = 0.028\n",
    "grid_tariff_low = 0.013333333\n",
    "energy_tariff_high = 0.148933333\n",
    "energy_tariff_low = 0.087613333\n",
    "res_incentive = 0.014\n",
    "\n",
    "high_tariff = grid_tariff_high + energy_tariff_high + res_incentive\n",
    "low_tariff = grid_tariff_low + energy_tariff_low + res_incentive\n",
    "\n",
    "del grid_tariff_high, energy_tariff_high, grid_tariff_low, energy_tariff_low, res_incentive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bd18a4fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Day Tariff</th>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5   \\\n",
       "Day Tariff  0.114947  0.114947  0.114947  0.114947  0.114947  0.114947   \n",
       "\n",
       "                  6         7         8         9         10        11  \\\n",
       "Day Tariff  0.114947  0.190933  0.190933  0.190933  0.190933  0.190933   \n",
       "\n",
       "                  12        13        14        15        16        17  \\\n",
       "Day Tariff  0.190933  0.190933  0.190933  0.190933  0.190933  0.190933   \n",
       "\n",
       "                  18        19        20        21        22        23  \\\n",
       "Day Tariff  0.190933  0.190933  0.114947  0.114947  0.114947  0.114947   \n",
       "\n",
       "                  24  \n",
       "Day Tariff  0.114947  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tariffs = []\n",
    "for i in range(int(timesteps_per_episode) + 1):\n",
    "    if i < 7 / time_interval or i > 19 / time_interval:\n",
    "        tariffs.append(low_tariff)\n",
    "    else:\n",
    "        tariffs.append(high_tariff)\n",
    "\n",
    "day_tariffs = pd.DataFrame({'Day Tariff': tariffs})\n",
    "\n",
    "del tariffs, high_tariff, low_tariff\n",
    "day_tariffs.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2189bf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_initial_values(soc, arrivals, departures, capacities, requested_soc):\n",
    "    vehicles = [i for i in arrivals.columns]\n",
    "    \n",
    "    initial_data = pd.DataFrame()\n",
    "    for charger in range(number_of_chargers):\n",
    "        for vehicle in vehicles:\n",
    "            arrival = arrivals.loc[arrivals.index == charger, [vehicle]]\n",
    "            arrival = arrival[arrival.columns[0]].item()\n",
    "\n",
    "            if not pd.isna(arrival):\n",
    "                df = pd.DataFrame()\n",
    "                df['Arrival'] = [arrival]\n",
    "\n",
    "                departure = departures.loc[departures.index == charger, [vehicle]]\n",
    "                departure = departure[departure.columns[0]].item()\n",
    "                df['Departure'] = departure\n",
    "\n",
    "                df['Vehicle ID'] = str(vehicle)\n",
    "                df['Charger ID'] = str(charger)\n",
    "                \n",
    "                df['EV Capacity'] = capacities[charger][arrival]\n",
    "                \n",
    "                df['Initial State of Charge'] = soc[charger][arrival]\n",
    "                df['Requested State of Charge'] = requested_soc[charger][arrival]\n",
    "                \n",
    "                initial_data = pd.concat([initial_data.loc[:], df]).reset_index(drop=True)\n",
    "\n",
    "    initial_data = initial_data[['Arrival', 'Departure', 'EV Capacity', 'Initial State of Charge', \n",
    "                                 'Requested State of Charge', 'Vehicle ID', 'Charger ID']]\n",
    "    return initial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "876399f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(initial_values_path) as json_data:\n",
    "    initial_data = json.load(json_data)\n",
    "    \n",
    "    df_charger_occupancy = pd.DataFrame(initial_data['Charger_occupancy']).T\n",
    "    df_vehicle_capacities = pd.DataFrame(initial_data['Vehicle_capacities']).T\n",
    "    \n",
    "    initial_training_data = load_initial_values(soc=pd.DataFrame(initial_data['SOC']).T,\n",
    "                                                requested_soc=pd.DataFrame(initial_data['Requested_SOC']).T,\n",
    "                                                arrivals=pd.DataFrame(initial_data['Arrivals']),\n",
    "                                                departures=pd.DataFrame(initial_data['Departures']),\n",
    "                                                capacities=df_vehicle_capacities)\n",
    "    \n",
    "    del initial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "015f374f",
   "metadata": {},
   "outputs": [],
   "source": [
    "charger_occupancy = pd.DataFrame()\n",
    "\n",
    "for charger in df_charger_occupancy.columns:\n",
    "    temp_df = pd.DataFrame(df_charger_occupancy[charger])\n",
    "    temp_df.columns = ['Charger Occupation State']\n",
    "    temp_df['Charger ID'] = charger\n",
    "    charger_occupancy = pd.concat([charger_occupancy, temp_df])\n",
    "\n",
    "del df_charger_occupancy, temp_df\n",
    "# charger_occupancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3879fcd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_capacities = pd.DataFrame()\n",
    "\n",
    "for charger in df_vehicle_capacities.columns:\n",
    "    temp_df = pd.DataFrame(df_vehicle_capacities[charger])\n",
    "    temp_df.columns = ['Vehicle Capacities']\n",
    "    temp_df['Charger ID'] = charger\n",
    "    vehicle_capacities = pd.concat([vehicle_capacities, temp_df])\n",
    "\n",
    "del df_vehicle_capacities, temp_df\n",
    "# vehicle_capacities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8cba11",
   "metadata": {},
   "source": [
    "Predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ca877690",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(prediction_results_path) as json_data:\n",
    "    prediction_data = json.load(json_data)\n",
    "    \n",
    "    df_soc = pd.DataFrame(prediction_data['SOC']).T\n",
    "    df_charger_actions = pd.DataFrame(prediction_data['Charger_actions'])\n",
    "    df_charger_power_values = pd.DataFrame(prediction_data['Charger_power_values'])\n",
    "    \n",
    "    chargers_predictions = pd.DataFrame()\n",
    "    for charger in df_soc.columns:\n",
    "        temp_df = pd.DataFrame(df_soc[charger])\n",
    "        temp_df.columns = ['Vehicle State of Charge']\n",
    "        temp_df['Charger Action'] = df_charger_actions[charger]\n",
    "        temp_df['Charger Power Value'] = df_charger_power_values[charger]\n",
    "        temp_df['Charger ID'] = charger\n",
    "        chargers_predictions = pd.concat([chargers_predictions, temp_df])\n",
    "        del temp_df\n",
    "    \n",
    "    del df_soc, df_charger_actions, df_charger_power_values\n",
    "    \n",
    "    if 'Initial_battery_state_of_charge' in prediction_data:\n",
    "        initial_battery_soc = float(prediction_data['Initial_battery_state_of_charge']) \n",
    "    else: \n",
    "        initial_battery_soc = 0.5\n",
    "    df_battery_soc = pd.DataFrame(prediction_data['Battery_state_of_charge'])\n",
    "    df_battery_action = pd.DataFrame(prediction_data['Battery_action'])\n",
    "    df_battery_power_value = pd.DataFrame(prediction_data['Battery_power_value'])\n",
    "    df_battery_calculated_power_value = pd.DataFrame(prediction_data['Battery_calculated_power_value'])\n",
    "    \n",
    "    battery_predictions= pd.DataFrame()\n",
    "    battery_predictions['Battery SOC'] = df_battery_soc\n",
    "    battery_predictions['Battery Action'] = df_battery_action\n",
    "    battery_predictions['Battery Power Value'] = df_battery_power_value\n",
    "    battery_predictions['Battery Calculated Power Value'] = df_battery_calculated_power_value\n",
    "    \n",
    "    del df_battery_soc, df_battery_action, df_battery_power_value, df_battery_calculated_power_value\n",
    "\n",
    "    df_total_cost = pd.DataFrame(prediction_data['Total_cost'])\n",
    "    df_total_reward = df_total_cost * (-1)\n",
    "    df_total_penalties = pd.DataFrame(prediction_data['Total_penalties'])\n",
    "    df_grid_energy_cost = pd.DataFrame(prediction_data['Grid_energy_cost'])\n",
    "    \n",
    "    costs_predictions = pd.DataFrame()\n",
    "    costs_predictions['Total Cost'] = df_total_cost\n",
    "    costs_predictions['Total Reward'] = df_total_reward\n",
    "    costs_predictions['Total Penalty'] = df_total_penalties\n",
    "    costs_predictions['Grid Energy Cost'] = df_grid_energy_cost\n",
    "    costs_predictions['Electricity Day Tariffs'] = day_tariffs\n",
    "    \n",
    "    del df_total_cost, df_total_reward, df_grid_energy_cost, day_tariffs\n",
    "    \n",
    "    df_total_vehicle_penalties = pd.DataFrame(prediction_data['Total_vehicle_penalties'])\n",
    "    df_insufficient_vehicle_charging_penalties = pd.DataFrame(prediction_data['Insufficiently_charged_vehicle_penalties'])\n",
    "    df_needless_vehicle_charging_penalties = pd.DataFrame(prediction_data['Needlessly_charged_vehicle_penalties'])\n",
    "    df_overcharged_vehicle_penalties = pd.DataFrame(prediction_data['Overcharged_vehicle_penalties'])\n",
    "    df_over_discharged_vehicle_penalties = pd.DataFrame(prediction_data['Over_discharged_vehicle_penalties'])\n",
    "    df_total_battery_penalties = pd.DataFrame(prediction_data['Total_battery_penalties'])\n",
    "    df_battery_soc_below_dod_penalties = pd.DataFrame(prediction_data['Battery_SOC_below_DoD_penalties'])\n",
    "    df_battery_overcharging_penalties = pd.DataFrame(prediction_data['Battery_overcharging_penalties'])\n",
    "    df_battery_over_discharging_penalties = pd.DataFrame(prediction_data['Battery_over_discharging_penalties'])\n",
    "    df_low_resource_utilisation_penalties = pd.DataFrame(prediction_data['Low_resource_utilisation_penalties'])\n",
    "    df_discharging_nonexistent_vehicles_penalties = pd.DataFrame(prediction_data['DisCharging_nonexistent_vehicles_penalties'])\n",
    "#     df_excess_battery_charging_penalties = pd.DataFrame(prediction_data['Excessively_charged_battery_penalties'])\n",
    "#     df_excess_battery_discharging_penalties = pd.DataFrame(prediction_data['Excessively_discharged_battery_penalties'])\n",
    "    \n",
    "    penalties = pd.DataFrame()\n",
    "    penalties['Total Penalty'] = df_total_penalties\n",
    "    penalties['Total Vehicle Penalty'] = df_total_vehicle_penalties\n",
    "    penalties['Insufficiently Charged Vehicle Penalty'] = df_insufficient_vehicle_charging_penalties\n",
    "    penalties['Needless Vehicle Charging Penalty'] = df_needless_vehicle_charging_penalties\n",
    "    penalties['Overcharged Vehicle Penalty'] = df_overcharged_vehicle_penalties\n",
    "    penalties['Over Discharged Vehicle Penalty'] = df_over_discharged_vehicle_penalties\n",
    "    penalties['Total Battery Penalty'] = df_total_battery_penalties\n",
    "    penalties['Battery SOC Below DoD Penalty'] = df_battery_soc_below_dod_penalties\n",
    "    penalties['Battery Overcharging Penalty'] = df_battery_overcharging_penalties\n",
    "    penalties['Battery Over Discharging Penalty'] = df_battery_over_discharging_penalties\n",
    "    penalties['Low Resource Utilisation Penalty'] = df_low_resource_utilisation_penalties\n",
    "    penalties['DisCharging Nonexistent Vehicles Penalty'] = df_discharging_nonexistent_vehicles_penalties\n",
    "    \n",
    "    del df_total_penalties, df_total_vehicle_penalties, df_total_battery_penalties, df_overcharged_vehicle_penalties\n",
    "    del df_insufficient_vehicle_charging_penalties, df_over_discharged_vehicle_penalties\n",
    "    del df_needless_vehicle_charging_penalties, df_battery_soc_below_dod_penalties, df_low_resource_utilisation_penalties\n",
    "    del df_discharging_nonexistent_vehicles_penalties, df_battery_overcharging_penalties\n",
    "    del df_battery_over_discharging_penalties\n",
    "    \n",
    "    df_grid_power = pd.DataFrame(prediction_data['Grid_power'])\n",
    "    df_grid_energy = pd.DataFrame(prediction_data['Grid_energy'])\n",
    "    df_utilised_solar_energy = pd.DataFrame(prediction_data['Utilized_solar_energy'])\n",
    "    df_available_solar_energy = pd.DataFrame(prediction_data['Available_solar_energy']).T\n",
    "    df_total_charging_power = pd.DataFrame(prediction_data['Total_charging_power'])\n",
    "    df_total_discharging_power = pd.DataFrame(prediction_data['Total_discharging_power'])\n",
    "    \n",
    "    energy_predictions = pd.DataFrame()\n",
    "    energy_predictions['Grid Power'] = df_grid_power\n",
    "    energy_predictions['Grid Energy'] = df_grid_energy\n",
    "    energy_predictions['Utilised Solar Power'] = df_utilised_solar_energy\n",
    "    \n",
    "    if len(df_available_solar_energy) > 1:\n",
    "        energy_predictions['Available Solar Energy - 0-24h'] = df_available_solar_energy.loc[0:24]\n",
    "        energy_predictions['Available Solar Energy - 24-48h'] = df_available_solar_energy.loc[24:47][0].tolist()\n",
    "    else:\n",
    "        energy_predictions['Available Solar Energy - 0-24h'] = [0]*24\n",
    "        energy_predictions['Available Solar Energy - 24-48h'] = [0]*24    \n",
    "    \n",
    "    energy_predictions['Total Charging Power'] = df_total_charging_power\n",
    "    energy_predictions['Total Discharging Power'] = df_total_discharging_power\n",
    "    \n",
    "    del df_grid_power, df_grid_energy, df_utilised_solar_energy, df_available_solar_energy\n",
    "    del df_total_charging_power, df_total_discharging_power\n",
    "    \n",
    "    del prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "09233138",
   "metadata": {},
   "outputs": [],
   "source": [
    "# chargers_predictions.T\n",
    "# battery_predictions.T\n",
    "# costs_predictions.T\n",
    "# penalties.T\n",
    "# energy_and_power_predictions.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ece1a74",
   "metadata": {},
   "source": [
    "# Initial values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c76d049",
   "metadata": {},
   "source": [
    "#### Initial Model Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c6c13f54",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "subplot_fig = make_subplots(rows=4, cols=1, shared_xaxes=True, vertical_spacing=0.02)\n",
    "\n",
    "max_ev_cap = vehicle_capacities['Vehicle Capacities'].max()\n",
    "\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    ev_cap_text = str(initial_training_data.loc[v_id, 'EV Capacity'])\n",
    "    \n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        subplot_fig.add_scatter(line_shape='hv',\n",
    "                                y=charger_occupancy.loc[charger_occupancy['Charger ID']==charger,'Charger Occupation State'], \n",
    "                                row=counter, col=1, name=f'Occupied', \n",
    "                                fillpattern=dict(shape='/', fgcolor='rgba(71, 164, 235, 0.5)', fgopacity=0.5),\n",
    "                                fillcolor=f'rgba(71, 164, 235, 0.4)', \n",
    "                                line_width=0, fill='tozeroy', \n",
    "                                legendgroup='Charger occupancy', showlegend=(not v_id), \n",
    "                                legendgrouptitle=dict(text='Charger'))\n",
    "        subplot_fig.add_scatter(line_shape='hv',\n",
    "                                y=vehicle_capacities.loc[vehicle_capacities['Charger ID']==charger,'Vehicle Capacities']/max_ev_cap, \n",
    "                                row=counter, col=1, name='Vehicle Capacity [kWh]',\n",
    "                                fillcolor=f'rgba(71, 164, 235, 0.5)', \n",
    "                                line_width=0, fill='tozeroy', legendgroup='Charger occupancy',\n",
    "                                showlegend=(not v_id))\n",
    "        \n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure'], \n",
    "                            row=counter, col=1,\n",
    "                            y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], \n",
    "                            name=f'Requested - on departure',\n",
    "                            width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                            legendgrouptitle=dict(text='State of Charge'))\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], \n",
    "                            row=counter, col=1,\n",
    "                            y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], \n",
    "                            name=f'Initial - on arrival', width=0.5, \n",
    "                            marker=dict(color='crimson', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id))\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, \n",
    "                                   row=charger_id+1, col=1, font_size=13, showarrow=False, font_color='grey')\n",
    "        \n",
    "    if dep > 24 and arr == 23:\n",
    "        subplot_fig.add_annotation(x=arr+0.5, y=0.4, text=f'<i>{ev_cap_text}</i>', textangle=-90, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    elif dep > 24 and arr == 22:\n",
    "        subplot_fig.add_annotation(x=(arr+24)/2, y=0.4, text=f'<i>{ev_cap_text}</i>', textangle=-90, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    elif dep > 24:\n",
    "        subplot_fig.add_annotation(x=(arr+24)/2, y=0.12, text=f'<i>{ev_cap_text}</i>', textangle=0, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    else:\n",
    "        subplot_fig.add_annotation(x=(arr+dep)/2, y=0.12, text=f'<i>{ev_cap_text}</i>', textangle=0, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    \n",
    "    subplot_fig.add_shape(type='line', x0=arr, x1=arr, y0=0, y1=1, layer='above',\n",
    "                          line_color='crimson', line_width=1.5, row=charger_id+1, col=1)\n",
    "    subplot_fig.add_shape(type='line', x0=dep, x1=dep, y0=0, y1=1, layer='above',\n",
    "                          line_color='teal', line_width=1.5, row=charger_id+1, col=1)\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.15, y=0.5, text=\"State of Charge\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Initial Model Info\", barmode='overlay')\n",
    "subplot_fig.update_xaxes(griddash=\"dot\", range=[-0.2, 24.2], tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 850\n",
    "plot_height = 650\n",
    "\n",
    "subplot_fig.update_yaxes(tickformat=\" ,~%\", nticks=5, range=[0, 1.1], dtick=0.25)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.43, font_size=24, y=0.9),\n",
    "                          showlegend=True, legend=dict(groupclick='togglegroup', x=1.03, y=0.97))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_ev_cap, charger_id, counter, charger, arr, dep, ev_cap_text, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e72183e",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce322af2",
   "metadata": {},
   "source": [
    "#### Predicted Vehicle State of Charge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "00d72704",
   "metadata": {
    "scrolled": false
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=4,\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.02\n",
    ")\n",
    "\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Vehicle State of Charge']\n",
    "        subplot_fig.add_scatter(y=psoc, line_shape='linear', row=counter, col=1, name='Value at timestep end', mode='markers+lines',\n",
    "                                marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkturquoise'), showlegend=(not v_id), \n",
    "                                legendgrouptitle=dict(text='Predicted State of Charge'), legendgroup='Predicted Charger SOC', )\n",
    "        subplot_fig.add_bar(y=psoc, row=counter, col=1, legendgroup='Predicted Charger SOC', showlegend=False, \n",
    "                            marker=dict(color=psoc, opacity=1, line_width=0, coloraxis='coloraxis'))\n",
    "        \n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure']-1, row=counter, col=1,\n",
    "                    y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], name=f'Requested - on departure',\n",
    "                    width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                    legendgrouptitle=dict(text='State of Charge'))\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], row=counter, col=1,\n",
    "                    y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], name=f'Initial - on arrival',\n",
    "                    width=0.5, marker=dict(color='crimson', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id))\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, row=charger_id+1, col=1,\n",
    "                                   font_size=13, showarrow=False, font_color='grey')    \n",
    "\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.15, y=0.5, text=\"State of Charge\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Predicted State of Charge per Charger\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.2],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 850\n",
    "plot_height = 650\n",
    "\n",
    "subplot_fig.update_yaxes(tickformat=\" ,~%\", nticks=5, range=[0, 1.1], dtick=0.25)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.42, font_size=24, y=0.9), \n",
    "                          showlegend=True, \n",
    "                          coloraxis=dict(\n",
    "                              colorbar=dict(len=0.7, thickness=14, yanchor='middle', y=0.35, x=1.025, \n",
    "                                            title='State of Charge', titleside='right', tickvals=[0, 0.25, 0.5, 0.75, 0.95, 0.98, 1],\n",
    "                                            tickformat=',.0%', ticktext=['0%','25%','50%','75%','95%','\\t  ] Optimal Value Range','100%']),\n",
    "                              colorscale=[[0, 'rgba(220,20,60, 0.45)'], [0.9, 'rgba(0,128,128, 0.45)'], [0.95, 'rgba(0,206,209, 0.45)'], [1, 'rgba(0,206,209, 0.45)']]\n",
    "                          )\n",
    "                         )\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del psoc, charger_id, counter, charger, arr, dep, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173a841b",
   "metadata": {},
   "source": [
    "#### Predicted Vehicle State of Charge and corresponding action and power values and penalties"
   ]
  },
  {
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   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=5,\n",
    "    row_heights=[0.17, 0.17, 0.17, 0.17, 0.32],\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.04,\n",
    "    specs=[[{'secondary_y': True}], [{'secondary_y': True}], [{'secondary_y': True}], [{'secondary_y': True}],\n",
    "           [{'secondary_y': True}]]\n",
    ")\n",
    "\n",
    "charger_patterns = ['', '/', '\\\\', '+']\n",
    "charger_markers = ['circle', 'cross', 'diamond', 'square']\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Vehicle State of Charge']\n",
    "        pactions = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Charger Action']\n",
    "        ppowers = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Charger Power Value']\n",
    "        \n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure']-1, row=counter, col=1,\n",
    "                    y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], name=f'Requested - on departure',\n",
    "                    width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                    legendgrouptitle=dict(text='State of Charge'), secondary_y=False)\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], row=counter, col=1,\n",
    "                    y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], name=f'Initial - on arrival',\n",
    "                    width=0.5, marker=dict(color='crimson', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id), secondary_y=False)\n",
    "        \n",
    "        subplot_fig.add_scatter(y=psoc, line_shape='linear', row=counter, col=1, name='Value at timestep end', mode='markers+lines',\n",
    "                                marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkturquoise'), \n",
    "                                showlegend=(not v_id), secondary_y=False,\n",
    "                                legendgrouptitle=dict(text='Predicted State of Charge'), legendgroup='Predicted Charger SOC')\n",
    "        subplot_fig.add_bar(y=pactions, row=counter, col=1, legendgroup='Charger Action', showlegend=True, name=f'Charger {charger}', legendgrouptitle=dict(text='Charging Action'),\n",
    "                            marker=dict(color='darkturquoise', opacity=0.45, line_width=1, line_color='darkturquoise',\n",
    "                                        pattern_shape=charger_patterns[charger], pattern_fgopacity=0.9, pattern_fgcolor='white', pattern_size=10), secondary_y=False)\n",
    "        \n",
    "        \n",
    "        \n",
    "        subplot_fig.add_scatter(y=ppowers, line_shape='linear', row=counter, col=1, name=f'Charger {charger}', mode='markers',\n",
    "                                marker=dict(size=8, symbol=charger_markers[charger]), line=dict(color='darkorange'), \n",
    "                                showlegend=True, secondary_y=True,\n",
    "                                legendgrouptitle=dict(text='Power Values'), legendgroup='Charger Power')\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, row=charger_id+1, col=1,\n",
    "                                   font_size=13, showarrow=False, font_color='grey')    \n",
    "\n",
    "pevpenalties = penalties['Total Vehicle Penalty']\n",
    "subplot_fig.add_scatter(y=pevpenalties, line_shape='linear', row=5, col=1, name='Value at timestep end', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='crimson'), fill='tozeroy',\n",
    "                        showlegend=True, secondary_y=False,\n",
    "                        legendgrouptitle=dict(text='Total Vehicle Penalty'), legendgroup='Total Vehicle Penalty')\n",
    "subplot_fig.add_bar(x=penalties.index, y=pevpenalties, row=5, col=1, name='Value at timestep end', secondary_y=False,\n",
    "                        width=0.2, legendgrouptitle=dict(text='Total Battery Penalty'), legendgroup='Total Battery Penalty',\n",
    "                        marker=dict(line_width=0, opacity=0.8, color='crimson'), showlegend=False)\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.14, y=0.65, yanchor='middle', text=\"State of Charge - Action Value\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.04, y=0.65, text=\"Power [kW]\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=12/26, y=-0.08, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Predicted State of Charge with Action and Power Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", range=[-0.4, 24.2],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 880\n",
    "plot_height = 890\n",
    "\n",
    "total_ev_pen_max = pevpenalties.max()\n",
    "y9type = 'log' if total_ev_pen_max > 100000 else 'linear'\n",
    "y9max_margin = 6 if y9type == 'log' else round(total_ev_pen_max + 10)\n",
    "y9yaxistext = '- log scale' if y9type == 'log' else ''\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.92), \n",
    "                          showlegend=True, legend=dict(x=26/24), margin=dict(autoexpand=True),\n",
    "                          xaxis4=dict(showticklabels=True),\n",
    "                          yaxis=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.2),\n",
    "                          yaxis2=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis3=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis4=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis5=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis6=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis7=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis8=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis9=dict(tickformat=\" ,~g\", nticks=9, type=y9type, \n",
    "                                      title=dict(text=f'Penalty Value {y9yaxistext}')))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del psoc, charger_id, counter, charger, arr, dep, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40bad29f",
   "metadata": {},
   "source": [
    "#### Vehicle Prediction Penalties per Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d1fd1fc9",
   "metadata": {},
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    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Insufficiently Charged Vehicle Penalty']\n",
    "pen2 = penalties['Overcharged Vehicle Penalty']\n",
    "pen3 = penalties['Needless Vehicle Charging Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen1, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.8, line_width=0), name='Insufficiently Charged Vehicle')\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.8, line_width=0), name='Overcharged Vehicle')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.8, line_width=0), name='Needless Vehicle Charging')\n",
    "\n",
    "pevpenalties = penalties['Total Vehicle Penalty']\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pevpenalties, line_shape='linear', name='Total Vehicle penalty', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='diamond', line=dict(width=1.5), color=pevpenalties, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)', width=2),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Vehicle Penalty')\n",
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    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Vehicle Prediction Penalties\",\n",
    "                          yaxis=dict(title=''))\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 950\n",
    "plot_height = 650\n",
    "\n",
    "subplot_fig.update_yaxes(nticks=6, dtick=0.2)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'orange'], [1, 'sienna']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Vehicle Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", dtick=5, type='linear'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9c9939",
   "metadata": {},
   "source": [
    "#### Predicted Battery System Values and Penalties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "afe7dfab",
   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=2,\n",
    "    row_heights=[0.6, 0.4],\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.04,\n",
    "    specs=[[{'secondary_y': True}], [{'secondary_y': True}]]\n",
    ")\n",
    "\n",
    "subplot_fig.add_scatter(y=[0.5], yaxis='y', row=1, col=1, secondary_y=False,\n",
    "                        x=[0], line_shape='linear', name='Initial State of Charge %', mode='markers',\n",
    "                        marker=dict(size=8, symbol='square-dot'), line=dict(color='darkorange'), showlegend=True)\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery SOC'], yaxis='y', row=1, col=1, secondary_y=False,\n",
    "                        x=battery_predictions.index+1, line_shape='linear', name='Current State of Charge %', mode='markers+lines',\n",
    "                        marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkorange'), showlegend=True)\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Action'], showlegend=True, yaxis='y2', width=0.9, row=1, col=1, secondary_y=False,\n",
    "                    marker=dict(color='darkturquoise', coloraxis='coloraxis', opacity=0.3, line_width=0), name='Battery Action')\n",
    "\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Calculated Power Value'], showlegend=True, yaxis='y3', width=0.9, row=1, col=1, secondary_y=True,\n",
    "                    marker=dict(color='dimgrey', pattern=dict(shape='\\\\', fillmode='replace', size=5), opacity=0.8, line_width=1.2, line_color='dimgrey'), name='Dis/Charging Calculated Power [kW]')\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Power Value'], showlegend=True, yaxis='y3', width=0.9, row=1, col=1, secondary_y=True,\n",
    "                    marker=dict(color='dimgrey', pattern=dict(shape='/', fillmode='replace', size=15), opacity=0.8, line_width=1.2, line_color='dimgrey'), name='Dis/Charging Power [kW]')\n",
    "\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery Calculated Power Value'], line_shape='linear', secondary_y=True, \n",
    "                        name='Dis/Charging Calculated Power [kW]', mode='markers', yaxis='y3', row=1, col=1,\n",
    "                        marker=dict(size=8, symbol='cross'), line=dict(color='black'), showlegend=True)\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery Power Value'], line_shape='linear', secondary_y=True,\n",
    "                        name='Dis/Charging Power [kW]', mode='markers', yaxis='y3', row=1, col=1,\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='black'), showlegend=True)\n",
    "\n",
    "pbpenalties = penalties['Total Battery Penalty']\n",
    "subplot_fig.add_scatter(y=pbpenalties, line_shape='linear', row=2, col=1, name='Value at timestep end', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='crimson'), fill='tozeroy',\n",
    "                        showlegend=False, secondary_y=False,\n",
    "                        )\n",
    "subplot_fig.add_bar(x=penalties.index, y=pbpenalties, row=2, col=1, name='Value at timestep end', secondary_y=False,\n",
    "                        width=0.2, legendgrouptitle=dict(text='Total Battery Penalty'), legendgroup='Total Battery Penalty',\n",
    "                        marker=dict(line_width=0, opacity=0.8, color='crimson', coloraxis='coloraxis'), showlegend=True)\n",
    "\n",
    "subplot_fig.add_annotation(x=12/26, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Predicted Battery Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 950\n",
    "plot_height = 790\n",
    "\n",
    "total_pen_max = pbpenalties.max()\n",
    "y3type = 'log' if total_pen_max > 1000000 else 'linear'\n",
    "y3max_margin = 6 if y3type == 'log' else round(total_pen_max + total_pen_max/10)\n",
    "y3yaxistext = '- log scale' if y3type == 'log' else ''\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=12/32, font_size=24, y=0.92), \n",
    "                          showlegend=True, legend=dict(x=26/24),\n",
    "                          yaxis=dict(tickformat=\" ,~\", nticks=5, range=[-1.1, 1.1], dtick=0.25, \n",
    "                                     title='State of Charge - Action Value'),\n",
    "                          yaxis2=dict(tickformat=\" ,~\", nticks=5, range=[-50.2, 50.2], dtick=10, title='Power [kW]'),\n",
    "                          yaxis3=dict(tickformat=\" ,~g\", nticks=6, type=y3type, \n",
    "                                      title=dict(text=f'Penalty Value {y3yaxistext}')))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c87b2de",
   "metadata": {},
   "source": [
    "#### Battery Prediction Penalties per Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1799fa3f",
   "metadata": {},
   "outputs": [
    {
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         "marker": {
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          "line": {
           "width": 0
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          "opacity": 0.9
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         "name": "Battery SOC Below DoD",
         "showlegend": true,
         "type": "bar",
         "width": 0.9,
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          "line": {
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         "marker": {
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         "name": "Battery Over Discharging",
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   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Battery SOC Below DoD Penalty']\n",
    "pen2 = penalties['Battery Overcharging Penalty']\n",
    "pen3 = penalties['Battery Over Discharging Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen1, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.9, line_width=0), name='Battery SOC Below DoD')\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.9, line_width=0), name='Battery Overcharging')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.9, line_width=0), name='Battery Over Discharging')\n",
    "\n",
    "pbpenalties = penalties['Total Battery Penalty']\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pbpenalties, line_shape='linear', name='Total Battery penalty', mode='markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=pbpenalties, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Battery Penalty')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Battery Prediction Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_pen_max = pbpenalties.max()\n",
    "ytype = 'log' if total_pen_max > 100000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_pen_max + total_pen_max)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6, dtick=0.2)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'gold'], [1, 'darkgoldenrod']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Battery Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, dtick=ydtick, type=f'{ytype}'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9239ba67",
   "metadata": {},
   "source": [
    "#### Total Prediction Penalties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0f77873f",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Total Penalty']\n",
    "pen2 = penalties['Total Vehicle Penalty']\n",
    "pen3 = penalties['Total Battery Penalty']\n",
    "pen4 = penalties['DisCharging Nonexistent Vehicles Penalty']\n",
    "pen5 = penalties['Low Resource Utilisation Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.9, line_width=0), name='Total Vehicle Penalty')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.9, line_width=0), name='Total Battery Penalty')\n",
    "subplot_fig.add_bar(y=pen4, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='tan', opacity=0.9, line_width=0), name='DisCharging Nonexistent Vehicles Penalty')\n",
    "subplot_fig.add_bar(y=pen5, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkgoldenrod', opacity=0.8, line_width=0), name='Low Resource Utilisation Penalty')\n",
    "\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pen1, line_shape='linear', name='Total penalty', mode='lines+markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=pen1, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Penalty')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_pen1_max = pen1.max()\n",
    "ytype = 'log' if total_pen1_max > 100000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_pen1_max + total_pen1_max/10)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'gold'], [1, 'darkgoldenrod']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303400b1",
   "metadata": {},
   "source": [
    "#### Prediction Reward - Costs and Penalties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "100860b8",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "total_cost = costs_predictions['Total Cost']\n",
    "total_penalty = costs_predictions['Total Penalty']\n",
    "grid_cost = costs_predictions['Grid Energy Cost']\n",
    "\n",
    "subplot_fig.add_bar(y=total_penalty, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.9, line_width=1, line_color='crimson'), name='Total Penalty')\n",
    "subplot_fig.add_bar(y=grid_cost, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='silver', opacity=0.8, line_width=1, line_color='grey', pattern=dict(shape='/', fillmode='overlay', size=7)), name='Grid Energy Cost')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=total_cost, line_shape='linear', name='Total Cost', mode='markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=total_cost, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Cost')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Cost & Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Costs & Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_cost_max = total_cost.max()\n",
    "ytype = 'log' if total_cost_max > 10000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_cost_max + total_cost_max/10)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6, dtick=0.2)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'rgb(22, 22, 22)'], [0.25, 'dimgrey'], [0.5, 'lightgrey'], [0.7, '#E93556'], [0.9, 'crimson'], [1, '#B81432']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Cost', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, dtick=ydtick, type=f'{ytype}'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22913fe",
   "metadata": {},
   "source": [
    "#### Prediction Power Values"
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  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "65b14d0f",
   "metadata": {},
   "outputs": [
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         "line": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "grid_p = energy_predictions['Grid Power']\n",
    "pv_p = energy_predictions['Utilised Solar Power']\n",
    "total_ev_p = energy_predictions['Total Charging Power'] + energy_predictions['Total Discharging Power']\n",
    "battery_p = battery_predictions['Battery Power Value']\n",
    "day_tariff = costs_predictions['Electricity Day Tariffs']\n",
    "\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Peak Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=grid_p, line_shape='linear', name='Grid Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='turquoise', coloraxis='coloraxis'), line=dict(color='turquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Grid Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=pv_p, line_shape='linear', name='Utilised Solar Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='gold', coloraxis='coloraxis'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Solar Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=total_ev_p, line_shape='linear', name='Total Vehicle Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Power')\n",
    "subplot_fig.add_scatter(x=battery_predictions.index, y=battery_p, line_shape='linear', name='Total Battery Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='crimson', coloraxis='coloraxis'), line=dict(color='crimson'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Power')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Electricity Day Tariff', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Day Tariff')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Power [kW]\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.07, y=0.75, text=\"Grid Tariff\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Power Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24],\n",
    "                         tickvals=[i for i in range(max_timestep)], ticktext=[str(i%24) for i in range(max_timestep)], \n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "max_p = max(grid_p.max(), pv_p.max(), total_ev_p.max(), battery_p.max())\n",
    "power_margin = int(round(max_p/10)*10)+5\n",
    "power_ticks = int(power_margin/5)\n",
    "max_tariff = day_tariff.max()\n",
    "tariff_margin = int(max_tariff)+0.25\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'lightgrey'], [1, 'grey']], \n",
    "                                                          colorbar=dict(title=dict(text='', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", range=[-power_margin, power_margin], dtick=power_ticks, showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', nticks=11, dtick=0.05, tickmode='array', tickvals=[str(i/20) for i in range(12)],\n",
    "                                      autoshift=True, shift=2, side='right', range=[-tariff_margin, tariff_margin], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(overlaying='y',\n",
    "                                      range=[0, tariff_margin], visible=False))\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8578636a",
   "metadata": {},
   "source": [
    "#### Prediction Power and separate State of Charge Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "64279998",
   "metadata": {},
   "outputs": [
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   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "grid_p = energy_predictions['Grid Power']\n",
    "pv_p = energy_predictions['Utilised Solar Power']\n",
    "total_ev_p = energy_predictions['Total Charging Power'] + energy_predictions['Total Discharging Power']\n",
    "battery_soc = battery_predictions['Battery SOC']\n",
    "day_tariff = costs_predictions['Electricity Day Tariffs']\n",
    "\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Peak Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=grid_p, line_shape='linear', name='Grid Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='turquoise', coloraxis='coloraxis'), line=dict(color='turquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Grid Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=pv_p, line_shape='linear', name='Utilised Solar Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='gold', coloraxis='coloraxis'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Solar Power')\n",
    "\n",
    "subplot_fig.add_scatter(x=[0, 1], y=[0.5, battery_soc[0]], line_shape='linear', name='Initial Battery State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=False, yaxis='y3', legendgroup='Battery SOC')\n",
    "subplot_fig.add_scatter(x=battery_predictions.index+1, y=battery_soc, line_shape='linear', name='Battery State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=True, yaxis='y3', legendgroup='Battery SOC')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Electricity Day Tariff', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Day Tariff')\n",
    "\n",
    "color = ['yellow', 'orange', 'skyblue', 'chocolate']\n",
    "for ca in range(number_of_chargers):\n",
    "    psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==ca,'Vehicle State of Charge']\n",
    "    subplot_fig.add_scatter(x=battery_predictions.index, y=psoc, line_shape='linear', name=f'Charger {ca} State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color=ca, coloraxis='coloraxis'), line=dict(color=f'{color[ca]}'),\n",
    "                        showlegend=True, yaxis='y3')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Power [kW]\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.065, y=0.75, text=\"Grid Tariff\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.14, y=0.75, text=\"State of Charge\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Power & State of Charge Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24],\n",
    "                         tickvals=[i for i in range(max_timestep)], ticktext=[str(i%24) for i in range(max_timestep)], \n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 1000\n",
    "plot_height = 650\n",
    "\n",
    "max_p = max(grid_p.max(), pv_p.max(), total_ev_p.max(), battery_p.max())\n",
    "power_margin = int(round(max_p/10)*10)+5\n",
    "power_ticks = int(power_margin/5)\n",
    "max_tariff = day_tariff.max()\n",
    "tariff_margin = int(max_tariff)+0.25\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), margin=dict(r=180, t=150),\n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'lightgrey'], [1, 'grey']],\n",
    "                                                          colorbar=dict(title=dict(text='', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.05, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", range=[-power_margin*1.2, power_margin*1.2], dtick=power_ticks, showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', nticks=11, dtick=0.05, tickmode='array', tickvals=[str(i/20) for i in range(12)],\n",
    "                                      autoshift=True, shift=2, side='right', range=[-tariff_margin*1.2, tariff_margin*1.2], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(overlaying='y', tickformat='~%', autoshift=True, shift=25, side='right', anchor='free',\n",
    "                                      range=[-1*1.2, 1*1.2], tickmode='array', tickvals=[str(i/100) for i in range(0, 120, 20)]))\n",
    "\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ea8495",
   "metadata": {},
   "source": [
    "#### Available Solar Energy and Utilised Solar Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9f9505f2",
   "metadata": {},
   "outputs": [
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      "application/vnd.plotly.v1+json": {
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          "format": {
           "currency": [
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        "plotlyServerURL": "https://plot.ly"
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         "mode": "lines+markers",
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    }
   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "upv = energy_predictions['Utilised Solar Power']\n",
    "apv = energy_predictions['Available Solar Energy - 0-24h']\n",
    "\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Peak Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=upv, line_shape='linear', name='Utilised Solar Power', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle', line=dict(width=0.5), color='darkturquoise'), line=dict(color='darkturquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='PV')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=apv, line_shape='linear', name='Available Solar Energy', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='diamond', line=dict(width=0.5), color='gold'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Solar')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Electricity Day Tariff', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y3', legendgroup='Day Tariff')\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Available Solar Energy and Utilised Solar Power\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title=\"Timestep (Hour) in a Day\", range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)],\n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "max_solar_power = energy_predictions['Utilised Solar Power'].max()\n",
    "solar_margin = int(round(max_solar_power/2)*2)+2\n",
    "\n",
    "max_solar_energy = energy_predictions['Available Solar Energy - 0-24h'].max()\n",
    "solar_e_margin = int(round(max_solar_energy/2)*2)+2\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, barmode='stack', margin=dict(r=180),\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", dtick=2, range=[0, solar_margin+5], title='Power [kW]', showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\" ,.2~g\", overlaying='y', dtick=2, range=[0, solar_e_margin+3], \n",
    "                                      side='right', title='Energy [kWh]', showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', dtick=0.02, title='Grid Tariff',\n",
    "                                      autoshift=True, shift=10, side='right', range=[0, tariff_margin], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2))\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5715dd7c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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