Document Type

Conference Paper

Subject Area

CPS Efficient Buildings, CPS Real-Time

Date of this Version

11-15-2016

Publication Source

Data Predictive Control for Peak Power Reduction

DOI

http://dx.doi.org/10.1145/2993422.2993582

Abstract

Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.

Keywords

Machine learning, Predictive control, Building control, Peak power reduction

Bib Tex

@InProceedings{Jain2016, author = {Jain, Achin and Mangharam, Rahul and Behl, Madhur}, title = {Data Predictive Control for Peak Power Reduction}, booktitle = {Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments}, year = {2016}, series = {BuildSys '16}, pages = {109--118}, publisher = {ACM}, acmid = {2993582}, doi = {10.1145/2993422.2993582}, isbn = {978-1-4503-4264-3}, location = {Palo Alto, CA, USA}, numpages = {10}, url = {http://doi.acm.org/10.1145/2993422.2993582}, }

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Date Posted: 17 September 2016

This document has been peer reviewed.