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Article

An AI-Powered System for Residential Demand Response

1
TEKNIKER, Basque Research and Technology Alliance (BRTA), Iñaki Goenaga 5, 20600 Eibar, Spain
2
School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
3
Institute Mihajlo Pupin, University of Belgrade, Volgina 15, 11060 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Academic Editor: Juan-Carlos Cano
Electronics 2021, 10(6), 693; https://doi.org/10.3390/electronics10060693
Received: 25 January 2021 / Revised: 23 February 2021 / Accepted: 11 March 2021 / Published: 16 March 2021
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of this operation enhancement, as it may contribute to the reduction of demand peaks and maximization of renewable energy exploitation while mitigating potential problems with grid stability. In this article, a system based on artificial intelligence that solves the complex multi-objective problem to bring demand response programs to the residential sector is proposed. Through the application of novel machine learning-based algorithms, a unique control loop is developed to help dwellers determine how and when to use their appliances. The feasibility and validity of the proposed system has been demonstrated in a real-world neighbourhood where a notable reduction and shift of electricity demand peaks has been achieved. Concretely, in accordance with extreme changes in the energy prices, the users have demonstrated the ability to shift their demand to periods with lower prices as well as reducing power consumption during periods with higher prices, thus fully translating the demand peak in time. View Full-Text
Keywords: demand response; demand flexibility; artificial intelligence; machine learning; energy savings; peak shaving demand response; demand flexibility; artificial intelligence; machine learning; energy savings; peak shaving
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MDPI and ACS Style

Esnaola-Gonzalez, I.; Jelić, M.; Pujić, D.; Diez, F.J.; Tomašević, N. An AI-Powered System for Residential Demand Response. Electronics 2021, 10, 693. https://doi.org/10.3390/electronics10060693

AMA Style

Esnaola-Gonzalez I, Jelić M, Pujić D, Diez FJ, Tomašević N. An AI-Powered System for Residential Demand Response. Electronics. 2021; 10(6):693. https://doi.org/10.3390/electronics10060693

Chicago/Turabian Style

Esnaola-Gonzalez, Iker, Marko Jelić, Dea Pujić, Francisco J. Diez, and Nikola Tomašević. 2021. "An AI-Powered System for Residential Demand Response" Electronics 10, no. 6: 693. https://doi.org/10.3390/electronics10060693

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