Version 3.0.0

Version 3.0.0 is a major new release.

The following major changes have been done:

• Electrochromic windows have been added. See Buildings.Rooms.Examples.ElectroChromicWindow.
• The models in Buildings.Fluid.Movers can now be configured to use three different control input signals: a continuous signal (depending on the model either normalized speed, speed in rpm, prescribed mass flow rate or prescribed head), discrete stages of these quantities, or on/off. The models also have been refactored to make their implementation clearer.
• The new package Buildings.Fluid.HeatPumps has been added. This package contains models for idealized heat pumps whose COP changes proportional to the change in COP of a Carnot cycle, with an optional correction for the part load efficiency.
• Various models, in particular in the package Buildings.Electrical, have been reformulated to comply with the Modelica Language Definition. All models comply with the pedantic Modelica check of Dymola.

Version 2.1.0

Version 2.1.0 is fully compatible with version 2.0.0.

It adds the package Buildings.Fluid.FMI that provides containers for exporting thermofluid flow components as FMUs. It also updates the temperature sensor to optionally simulate heat losses, and it contains bug fixes for the trace substance sensor if used without flow reversal.

Improvements have been made to various models to reduce the simulation time, and to Buildings.Examples.Tutorial.Boiler to simplify the control implementation.

For the complete list of changes, see the release notes.

Version 2.0.0

Version 2.0.0 is a major release that contains various new packages, models and improvements.

The following major additions have been done in version 2.0:

• A CFD model that is embedded in a thermal zone has been added. This model is implemented in Buildings.Rooms.CFD. The CFD model is an implementation of the Fast Fluid Dynamics code that allows three-dimensional CFD inside a thermal zone, coupled to building heat transfer, HVAC components and feedback control loops.
• A new package Buildings.Electrical has been added. This package allows studying buildings to electrical grid integration. It includes models for loads, transformers, cables, batteries, PV and wind turbines. Models exist for DC and AC systems with two- or three-phases that can be balanced and unbalanced. The models compute voltage, current, active and reactive power based on the quasi-stationary assumption or using the dynamic phasorial representation.
• The new package Buildings.Controls.DemandResponse contains models for demand response simulation.
• The new package Buildings.Controls.Predictors contains a data-driven model that predicts the electrical load of a building. The prediction can be done either using an average baseline or a linear regression with respect to outside temperature. For both, optionally a day-of adjustment can be made.

For the complete list of changes, see the release notes.

Version 2.0.0 Release Candidate 1

Version 2.0.0 is a major release that contains various new packages, models and improvements.

The following major additions have been done in version 2.0:

• A CFD model that is embedded in a thermal zone has been added. This model is implemented in Buildings.Rooms.CFD. The CFD model is an implementation of the Fast Fluid Dynamics code that allows three-dimensional CFD inside a thermal zone, coupled to building heat transfer, HVAC components and feedback control loops.
• A new package Buildings.Electrical has been added. This package allows studying buildings to electrical grid integration. It includes models for loads, transformers, cables, batteries, PV and wind turbines. Models exist for DC and AC systems with two- or three-phase that can be balanced and unbalanced. The models compute voltage, current, active and reactive power based on the quasi-stationary assumption or using the dynamic phasorial representation.
• The new package Buildings.Controls.DemandResponse contains models for demand response simulation.
• The new package Buildings.Controls.Predictors contains a data-driven model that predicts the electrical load of a building. The prediction can be done either using an average baseline or a linear regression with respect to outside temperature. For both, optionally a day-of adjustment can be made.