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https://hdl.handle.net/10316/103878
Título: | Design of Ensemble Forecasting Models for Home Energy Management Systems | Autor: | Bot, Karol Santos, Samira Laouali, Inoussa Ruano, Antonio Ruano, Maria da Graça |
Palavras-chave: | energy systems; machine learning; forecasting; energy management systems; multiobjective genetic algorithms; ensemble models; energy in buildings | Data: | 2021 | Editora: | MDPI | Projeto: | UID/EMS/50022/2020 Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020 |
Título da revista, periódico, livro ou evento: | Energies | Volume: | 14 | Número: | 22 | Resumo: | 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. | URI: | https://hdl.handle.net/10316/103878 | ISSN: | 1996-1073 | DOI: | 10.3390/en14227664 | Direitos: | openAccess |
Aparece nas coleções: | I&D CISUC - Artigos em Revistas Internacionais |
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Ficheiro | Descrição | Tamanho | Formato | |
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Design-of-ensemble-forecasting-models-for-home-energy-management-systems2021EnergiesOpen-Access.pdf | 10.95 MB | Adobe PDF | Ver/Abrir |
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