Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques

Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques

At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy sys...

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Main Authors: Karol Bot, Inoussa Laouali, António Ruano, Maria da Graça Ruano
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Energies
Subjects:
home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
Online Access:https://www.mdpi.com/1996-1073/14/18/5852
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spelling doaj-63d6bf77097344bb86b948c9e7fa46fe2021-09-26T00:05:35ZengMDPI AGEnergies1996-10732021-09-01145852585210.3390/en14185852Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control TechniquesKarol Bot0Inoussa Laouali1António Ruano2Maria da Graça Ruano3Faculty of Science & Technology, University of Algarve, 8005-294 Faro, PortugalFaculty of Science & Technology, University of Algarve, 8005-294 Faro, PortugalFaculty of Science & Technology, University of Algarve, 8005-294 Faro, PortugalFaculty of Science & Technology, University of Algarve, 8005-294 Faro, PortugalAt a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.https://www.mdpi.com/1996-1073/14/18/5852home energy management systemsbuilding energymodel-based predictive controlbranch-and-bound algorithmsensitivity analysisphotovoltaics
collection DOAJ
language English
format Article
sources DOAJ
author Karol Bot
Inoussa Laouali
António Ruano
Maria da Graça Ruano
spellingShingle Karol Bot
Inoussa Laouali
António Ruano
Maria da Graça Ruano
Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
Energies
home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
author_facet Karol Bot
Inoussa Laouali
António Ruano
Maria da Graça Ruano
author_sort Karol Bot
title Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_short Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_full Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_fullStr Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_full_unstemmed Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
title_sort home energy management systems with branch-and-bound model-based predictive control techniques
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-09-01
description At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.
topic home energy management systems
building energy
model-based predictive control
branch-and-bound algorithm
sensitivity analysis
photovoltaics
url https://www.mdpi.com/1996-1073/14/18/5852
work_keys_str_mv AT karolbot homeenergymanagementsystemswithbranchandboundmodelbasedpredictivecontroltechniques
AT inoussalaouali homeenergymanagementsystemswithbranchandboundmodelbasedpredictivecontroltechniques
AT antonioruano homeenergymanagementsystemswithbranchandboundmodelbasedpredictivecontroltechniques
AT mariadagracaruano homeenergymanagementsystemswithbranchandboundmodelbasedpredictivecontroltechniques
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