RL-HEMS: Reinforcement learning based home energy management system for HVAC energy optimization

Olivera Kotevska, Kuldeep Kurte, Jeffrey Munk, Travis Johnston, Evan McKee, Kalyan Perumalla, Helia Zandi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment's constraints using a model-free Reinforcement Learning (KL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning testbed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimised schedule for HVAC within an acceptable time-interval to satisfy the homeowner's comfort and minimize the energy usage. Our preliminary results showed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model-RL-HEMS.

Original languageEnglish
Title of host publicationASHRAE Transactions - 2020 ASHRAE Winter Conference
PublisherASHRAE
Pages421-429
Number of pages9
ISBN (Electronic)9781947192492
StatePublished - 2020
Event2020 ASHRAE Winter Conference - Orlando, United States
Duration: Feb 1 2020Feb 5 2020

Publication series

NameASHRAE Transactions
Volume126
ISSN (Print)0001-2505

Conference

Conference2020 ASHRAE Winter Conference
Country/TerritoryUnited States
CityOrlando
Period02/1/2002/5/20

Funding

This work is funded by the Department of Energy, Energy Efficiency and Renewable Energy Office under the Buildings Technologies Program.

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