Twin4Build - V1.1.2

Release notes

Bug fixes

• Fix import error caused by FMPY changes in recent release - see https://github.com/JBjoernskov/Twin4Build/issues/75

Energy Systems - Building Energy Monitoring - Python
Published by JBjoernskov 5 months ago

Twin4Build - v1.1.1

Release notes

Bug fixes

• Fix bug in validation print that show [FAILED] despite the model being valid - see https://github.com/JBjoernskov/Twin4Build/issues/73

Energy Systems - Building Energy Monitoring - Python
Published by JBjoernskov 8 months ago

Twin4Build - v1.1.0

Release notes

Deprecation

• camelCase variables (startTime, endTime, stepSize) in public API (Simulator.simulate(), Estimator.estimate(), Optimizer.optimize()) - renamed to snake_case (start_time, end_time, step_size) for consistency. camelCase naming will be supported until next minor release. https://github.com/JBjoernskov/Twin4Build/commit/a4b3592fc88c508f263a5fee876b7e6fc65dee7b
• dict format for 'parameter' argument in Estimator.estimate() has been changed to list format. Dict format will be supported until next minor release. https://github.com/JBjoernskov/Twin4Build/commit/3d7ee4bfb01e79228c4015b74557c14818774c24

Improvements

• input output matching assert in SimulationModel.add_connection(). Makes debugging much easier for users creating models manually. https://github.com/JBjoernskov/Twin4Build/commit/1f73ea73dc0004bdf65575564b6c4217d6c65f3c

Features

• All estimation algorithms now support finite difference estimation of jacobian

Energy Systems - Building Energy Monitoring - Python
Published by JBjoernskov 8 months ago

Twin4Build - v1.0.0

Release notes

This is the first release of Twin4Build, including the core modules:

• Model:
  The main container for your building system, components, and their connections. Use this class to assemble your digital twin from reusable components.

• Simulator:
  Runs time-based simulations of your Model, producing time series outputs for all components. Handles the simulation loop and time stepping.

• Translator:
  Automatically generates a Model from a semantic model (ontology-based building description) and maintains a link between these. Enables ontology-driven, automated model creation.

• Estimator:
  Performs parameter estimation (calibration) for your Model using measured data. Supports both least-squares and PyTorch-based optimization.

• Optimizer:
  Optimizes building operation by adjusting setpoints or control variables to minimize objectives or satisfy constraints, using gradient-based methods.

Energy Systems - Building Energy Monitoring - Python
Published by JBjoernskov 8 months ago