Document Type

Conference Paper

Subject Area

CPS Efficient Buildings, CPS Model-Based Design

Date of this Version

4-2016

Publication Source

ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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 in- volve deriving first principles based models which are ex- tremely 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 out- performs rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW 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% pre- diction accuracy for 8 buildings on Penn’s campus. We com- pare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy predic- tion.

Keywords

CPS, data-drive, machine learning, demand response, energy, buildings

Bib Tex

@ARTICLE {behl_ICCPS16, author = "Madhur Behl , Achin Jain and Rahul Mangharam", title = "Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems", journal = "ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)", year = "2016", month = "apr" }

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Date Posted: 15 January 2016

This document has been peer reviewed.