A framework for researching energy optimization of factory operations.
Project description
The ETA Ctrl framework provides a standardized interface for developing digital twins of factories or machines in a factory. It is designed to facilitate rolling horizon optimization, simulation, and interaction with factory systems. The framework is based on the Gymnasium environment and integrates seamlessly with tools like FMUs, Pyomo models, and live connections to real-world assets.
Documentation
Full Documentation can be found on the Documentation Page.
Overview
Core
`EtaCtrl`: Central controller for managing optimization workflows, including learning and execution processes.
Configuration
`Config`: Represents the configuration for an optimization run.
`RunInfo`: Handles paths and metadata for optimization runs.
Environment
Base Classes:
`BaseEnv`: Abstract base class for creating custom environments.
`LiveEnv`: Extends BaseEnv for live environments interacting with real-world systems.
`PyomoEnv`: Extends BaseEnv for environments using Model Predictive Control (MPC).
`SimEnv`: Extends BaseEnv for environments using FMU-based simulations.
Vectorization:
`NoVecEnv`: Custom vectorizer for environments that handle multithreading internally.
Simulation
`FMUSimulator`: Provides functionality for simulating FMUs (Functional Mock-up Units).
Time Series
`scenario_from_csv`: Imports and processes scenario data from CSV files.
`df_from_csv`: Reads time series data from a CSV file and returns it as a pandas DataFrame.
`df_resample`: Resamples the time index of a DataFrame to a specified frequency.
`df_interpolate`: Interpolates missing values in a DataFrame with a specified frequency.
State Management
`StateVar`: Represents a single variable in the state of an environment.
`StateConfig`: Configures the action and observation spaces based on StateVar instances.
Contributing
Please read the development guide before starting development on ETA Ctrl
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