Design of Ensemble Forecasting Models for Home Energy Management Systems

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home...

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Autores principales:	Karol Bot, Samira Santos, Inoussa Laouali, Antonio Ruano, Maria da Graça Ruano
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	energy systems machine learning forecasting energy management systems multi-objective genetic algorithms ensemble models Technology T
Acceso en línea:	https://doaj.org/article/f9f74cc3ed98459795574bde62ddd07b
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spelling	oai:doaj.org-article:f9f74cc3ed98459795574bde62ddd07b2021-11-25T17:27:40ZDesign of Ensemble Forecasting Models for Home Energy Management Systems10.3390/en142276641996-1073https://doaj.org/article/f9f74cc3ed98459795574bde62ddd07b2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7664https://doaj.org/toc/1996-1073The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.Karol BotSamira SantosInoussa LaoualiAntonio RuanoMaria da Graça RuanoMDPI AGarticleenergy systemsmachine learningforecastingenergy management systemsmulti-objective genetic algorithmsensemble modelsTechnologyTENEnergies, Vol 14, Iss 7664, p 7664 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy systems machine learning forecasting energy management systems multi-objective genetic algorithms ensemble models Technology T
spellingShingle	energy systems machine learning forecasting energy management systems multi-objective genetic algorithms ensemble models Technology T Karol Bot Samira Santos Inoussa Laouali Antonio Ruano Maria da Graça Ruano Design of Ensemble Forecasting Models for Home Energy Management Systems
description	The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.
format	article
author	Karol Bot Samira Santos Inoussa Laouali Antonio Ruano Maria da Graça Ruano
author_facet	Karol Bot Samira Santos Inoussa Laouali Antonio Ruano Maria da Graça Ruano
author_sort	Karol Bot
title	Design of Ensemble Forecasting Models for Home Energy Management Systems
title_short	Design of Ensemble Forecasting Models for Home Energy Management Systems
title_full	Design of Ensemble Forecasting Models for Home Energy Management Systems
title_fullStr	Design of Ensemble Forecasting Models for Home Energy Management Systems
title_full_unstemmed	Design of Ensemble Forecasting Models for Home Energy Management Systems
title_sort	design of ensemble forecasting models for home energy management systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f9f74cc3ed98459795574bde62ddd07b
work_keys_str_mv	AT karolbot designofensembleforecastingmodelsforhomeenergymanagementsystems AT samirasantos designofensembleforecastingmodelsforhomeenergymanagementsystems AT inoussalaouali designofensembleforecastingmodelsforhomeenergymanagementsystems AT antonioruano designofensembleforecastingmodelsforhomeenergymanagementsystems AT mariadagracaruano designofensembleforecastingmodelsforhomeenergymanagementsystems
_version_	1718412385941716992

Design of Ensemble Forecasting Models for Home Energy Management Systems

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