Machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements
| Autor: | Yue Zhang, Anurag. K. Srivastava, Diane Cook |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
building management systems
power engineering computing energy conservation demand side management learning (artificial intelligence) power consumption hvac power generation control machine learning algorithm activity-aware demand response energy savings comfort requirements peak hour power generation commercial-level dr residential-level dr dr controllers real-time activity information sensor data-driven activity-based controller air conditioning devices random forest machine energy consumption resident constraints heating-ventilation-air conditioning devices Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| Zdroj: | IET Smart Grid (2020) |
| Druh dokumentu: | article |
| ISSN: | 2515-2947 |
| DOI: | 10.1049/iet-stg.2019.0249 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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