alwaysbyx/e2e-DR-learning
Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.
This project helps energy managers and grid operators understand how energy storage systems, buildings, and electricity consumers respond to price signals or other incentives. It takes real-time electricity price data and building environmental data as input and outputs identified parameters for demand response models, which can then be used to forecast behavior. Power utilities, energy aggregators, and facility managers can use this to optimize energy use and participate in demand response programs more effectively.
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Use this if you need to accurately identify and forecast the behavior of various electricity consumers or energy assets for demand response programs.
Not ideal if you are looking for a general-purpose forecasting tool unrelated to demand response or energy system optimization.
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Language
Python
License
MIT
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Last pushed
Jul 07, 2022
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