Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems
arXiv:1601.05164 · doi:10.1109/ICCPS.2016.7479093
Abstract
Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are completely manual and rule-based or involve deriving first principles based models which are extremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by for a large DoE commercial reference building and leads to a curtailment of kW and over in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building's facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves to prediction accuracy for 8 buildings on Penn's campus. We compare DR-Advisor with other data driven methods and rank on ASHRAE's benchmarking data-set for energy prediction.
To appear in the proceedings of ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS) 2016