Open AccessMachine learning algorithm for activity‐aware demand response considering energy savings and comfort requirements
Author(s) -
Zhang Yue,
Srivastava Anurag. K.,
Cook Diane
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0249
Subject(s) - demand response , computer science , controller (irrigation) , sustainability , electricity , air conditioning , energy consumption , energy (signal processing) , environmental economics , real time computing , engineering , economics , mechanical engineering , ecology , agronomy , statistics , mathematics , electrical engineering , biology
Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial‐level DR, residential‐level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real‐time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data‐driven activity‐based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real‐time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.