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paper note for energy forecast.

  1. Dai, Y., & Zhao, P. (2020). A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization. Applied Energy, 279, 115332.
Novelty real-time price/holiday and non-holiday
previous-limitation the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid
data/code simulation of Singapore/https://data.mendeley.com/datasets/hvz7g6r3mw/2
  1. Dong, Y., Zhang, H., Wang, C., & Zhou, X. (2021). A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting. Applied Energy, 286, 116545.
Novelty The original wind power series is first decomposed into several intrinsic mode functions by complete ensemble empirical mode decomposition, and then a Bernstein polynomial forecasting model with mixture of Gaussians is constructed. Finally, a population- based multi-objective state transition algorithm with parallel search mechanism is developed to optimize the parameters of the hybrid model.
previous-limitation owing to the intermittence and nonlinearity of wind power time series/improve the accuracy and stability of wind power forecasting
data/code a wind farm in Xinjiang
  1. Buzna, L., De Falco, P., Ferruzzi, G., Khormali, S., Proto, D., Refa, N., ... & van der Poel, G. (2020). An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations. Applied Energy, 116337.
Novelty The hierarchical approach is applied to decompose the problem into sub-problems at low-level regions, which are resolved through standard probabilistic models
previous-limitation Accurate electric vehicle load forecasting becomes essential to reduce adverse effects of electric vehicle integration into the grid.
data/code EVnetNL dataset
  1. Mashlakov, A., Kuronen, T., Lensu, L., Kaarna, A., & Honkapuro, S. (2021). Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Applied Energy, 285, 116405. awesome
Novelty This study bridges the gap between the adoption of novel global deep-learning-based models for probabilistic multivariate forecasting in the deep learning community and the applicability of these methods for energy forecasting.
previous-limitation capturing the complex dependences and uncertainties of power system operation/the adoption of global deep learning models for multivariate energy forecasting in power systems is far behind the developments in the deep learning research field
data/code https://github.com/aleksei-mashlakov/multivariate-deep-learning
  1. Kong, W., Jia, Y., Dong, Z. Y., Meng, K., & Chai, S. (2020). Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting. Applied Energy, 280, 115875.

    Novelty deep whole-sky-image learning architectures for very short- term solar photovoltaic generation forecasting, of which the lookahead windows concern the scales from 4 to 20 min
    previous-limitation At the operating stage, the forecasting accuracy of renewables has a direct influence on energy scheduling and dispatching.
    data/code self-collected