Document Type

Conference Paper

Subject Area

CPS Efficient Buildings

Date of this Version

9-2015

Publication Source

Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale

Issue

EPFL-CONF-213354

Start Page

401

Last Page

406

Abstract

A data-driven method for demand response baselining and strategy evaluation is presented. Using meter and weather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be used for evaluating demand response strategies in real-time, without having to learn complex models of the building. The methods have been integrated in an open-source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager by advising on which control actions should be during a demand response event. We provide a case study using data from a large commercial vistural test-bed building to evaluate the performance of the DR-Advisor tool. Keywords: demand response, regression trees, machine learning

Keywords

Data-driven, CPS, demand response, buildings, modeling, control

Bib Tex

@ARTICLE {behl_CISBAT15, author = "Madhur Behl, Truong Nghiem and Rahul Mangharam", title = "DR-Advisor: A Data Driven Demand Response Recommender System", journal = "Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale", year = "2015", volume = "EPFL-CONF-213354", pages = "401-406", month = "sep" }

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Date Posted: 15 January 2016

This document has been peer reviewed.