Please use this identifier to cite or link to this item:
Title: Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques
Authors: Bot, Karol
Laouali, Inoussa
Ruano, António
Ruano, Maria da Graça 
Keywords: home energy management systems; building energy; model-based predictive control; branch-and-bound algorithm; sensitivity analysis; photovoltaics; battery
Issue Date: 2021
Publisher: MDPI
Project: Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 SAICT, grants 39578/2018 and 72581/2020 
FCT - UID/EMS/50022/2020 
Serial title, monograph or event: Energies
Volume: 14
Issue: 18
Abstract: 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.
ISSN: 1996-1073
DOI: 10.3390/en14185852
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
I&D IT - Artigos em Revistas Internacionais

Show full item record


checked on May 2, 2023

Page view(s)

checked on Sep 18, 2023


checked on Sep 18, 2023

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons