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Article

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

1
Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
2
SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez 1049-001, Morocco
3
IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal
4
CISUC, University of Coimbra, 3030-290 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Jose A. Afonso, João L. Afonso and Vítor Monteiro
Energies 2021, 14(18), 5852; https://doi.org/10.3390/en14185852
Received: 5 August 2021 / Revised: 11 September 2021 / Accepted: 13 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Smart Home Technologies Based on IoT Concepts)
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. View Full-Text
Keywords: home energy management systems; building energy; model-based predictive control; branch-and-bound algorithm; sensitivity analysis; photovoltaics; battery home energy management systems; building energy; model-based predictive control; branch-and-bound algorithm; sensitivity analysis; photovoltaics; battery
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MDPI and ACS Style

Bot, K.; Laouali, I.; Ruano, A.; Ruano, M.d.G. Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques. Energies 2021, 14, 5852. https://doi.org/10.3390/en14185852

AMA Style

Bot K, Laouali I, Ruano A, Ruano MdG. Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques. Energies. 2021; 14(18):5852. https://doi.org/10.3390/en14185852

Chicago/Turabian Style

Bot, Karol, Inoussa Laouali, António Ruano, and Maria d.G. Ruano 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques" Energies 14, no. 18: 5852. https://doi.org/10.3390/en14185852

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