Smart home energy management processes support through machine learning algorithms

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359531" target="_blank" >RIV/68407700:21230/22:00359531 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.egyr.2022.01.033" target="_blank" >https://doi.org/10.1016/j.egyr.2022.01.033</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.egyr.2022.01.033" target="_blank" >10.1016/j.egyr.2022.01.033</a>

Result language
angličtina
Original language name
Smart home energy management processes support through machine learning algorithms
Original language description
Smart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts. (C) 2022 The Author(s). Published by Elsevier Ltd.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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