An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building

This paper proposes an improved non-schedulable load forecasting (NLF) technique that can be utilized to enhance the performance of the energy management system (EMS) in a nearly zero energy building (nZEB). The suggested NLF is based on a long short-term memory (LSTM) framework in conjunction with...

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Autores principales:	Nikolaos Jabbour, Evangelos Tsioumas, Dimitrios Papagiannis, Markos Koseoglou, Christos Mademlis
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Forecasting energy management buildings Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a268a288cd6f4ebea3c529d6d046b654
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spelling	oai:doaj.org-article:a268a288cd6f4ebea3c529d6d046b6542021-11-18T00:00:52ZAn Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building2169-353610.1109/ACCESS.2021.3126900https://doaj.org/article/a268a288cd6f4ebea3c529d6d046b6542021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610057/https://doaj.org/toc/2169-3536This paper proposes an improved non-schedulable load forecasting (NLF) technique that can be utilized to enhance the performance of the energy management system (EMS) in a nearly zero energy building (nZEB). The suggested NLF is based on a long short-term memory (LSTM) framework in conjunction with a semi-supervised clustering (SSC) technique, considering the most important features which may affect the energy consumption of the non-schedulable appliances (NAs), i.e. number and identity of residents, energy consumption, weather, temperature, humidity, outdoor irradiance, correlation with other loads, day of the week and holidays. The SSC algorithm is utilized to fill the uncompleted information for the residents’ presence in the house and its output constitutes one of the inputs of the LSTM based technique which provides as output a set of forecasting sequences of the NAs’ energy consumption. Unlike the published techniques, the proposed NLF method is not only based on the modeling of the residents’ preferences and habits, but it considers them as variables which affect the nZEB’s microgrid and EMS performance. Therefore, it predicts the residents’ behavior considering its interdependence with the nZEB’s microgrid, which can considerably contribute to the enhancement of the EMS effectiveness and performance. For the implementation of the proposed NLF, no additional hardware is required, but only amendments in the EMS to consider the NLF’s outcomes. To validate the effectiveness of the proposed NLF, selective Hardware-in-the-Loop results from a real nZEB are presented.Nikolaos JabbourEvangelos TsioumasDimitrios PapagiannisMarkos KoseoglouChristos MademlisIEEEarticleForecastingenergy managementbuildingsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151931-151943 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Forecasting energy management buildings Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Forecasting energy management buildings Electrical engineering. Electronics. Nuclear engineering TK1-9971 Nikolaos Jabbour Evangelos Tsioumas Dimitrios Papagiannis Markos Koseoglou Christos Mademlis An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
description	This paper proposes an improved non-schedulable load forecasting (NLF) technique that can be utilized to enhance the performance of the energy management system (EMS) in a nearly zero energy building (nZEB). The suggested NLF is based on a long short-term memory (LSTM) framework in conjunction with a semi-supervised clustering (SSC) technique, considering the most important features which may affect the energy consumption of the non-schedulable appliances (NAs), i.e. number and identity of residents, energy consumption, weather, temperature, humidity, outdoor irradiance, correlation with other loads, day of the week and holidays. The SSC algorithm is utilized to fill the uncompleted information for the residents’ presence in the house and its output constitutes one of the inputs of the LSTM based technique which provides as output a set of forecasting sequences of the NAs’ energy consumption. Unlike the published techniques, the proposed NLF method is not only based on the modeling of the residents’ preferences and habits, but it considers them as variables which affect the nZEB’s microgrid and EMS performance. Therefore, it predicts the residents’ behavior considering its interdependence with the nZEB’s microgrid, which can considerably contribute to the enhancement of the EMS effectiveness and performance. For the implementation of the proposed NLF, no additional hardware is required, but only amendments in the EMS to consider the NLF’s outcomes. To validate the effectiveness of the proposed NLF, selective Hardware-in-the-Loop results from a real nZEB are presented.
format	article
author	Nikolaos Jabbour Evangelos Tsioumas Dimitrios Papagiannis Markos Koseoglou Christos Mademlis
author_facet	Nikolaos Jabbour Evangelos Tsioumas Dimitrios Papagiannis Markos Koseoglou Christos Mademlis
author_sort	Nikolaos Jabbour
title	An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
title_short	An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
title_full	An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
title_fullStr	An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
title_full_unstemmed	An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building
title_sort	improved non-schedulable load forecasting strategy for enhancing the performance of the energy management in a nearly zero energy building
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a268a288cd6f4ebea3c529d6d046b654
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An Improved Non-Schedulable Load Forecasting Strategy for Enhancing the Performance of the Energy Management in a Nearly Zero Energy Building

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