Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems

TL;DR

This paper introduces a data-driven control method for demand response in large buildings, outperforming traditional rule-based strategies and integrated into an open-source tool to optimize energy savings and operational efficiency.

Contribution

The paper presents the mbCRT algorithm for closed-loop demand response control, demonstrating significant improvements over rule-based methods and providing an open-source decision support tool.

Findings

Outperforms rule-based DR by 17% in large commercial buildings

Achieves 92.8% to 98.9% demand prediction accuracy

Ranks 2nd on ASHRAE energy prediction benchmark

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 $17\%$ for a large DoE commercial reference building and leads to a curtailment of $380$kW and over $\$45,000$ 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 $92.8\%$ to $98.9\%$ prediction accuracy for 8 buildings on Penn's campus. We compare DR-Advisor with other data driven methods and rank $2^{nd}$ on ASHRAE's benchmarking data-set for energy prediction.