{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:05:59Z","timestamp":1774159559839,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/EMS\/50022\/2020"],"award-info":[{"award-number":["UID\/EMS\/50022\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Portugal 2020","award":["SAICT 72581\/2020"],"award-info":[{"award-number":["SAICT 72581\/2020"]}]},{"name":"Portugal 2020","award":["SAICT 39578\/2018"],"award-info":[{"award-number":["SAICT 39578\/2018"]}]},{"name":"Operational Program CRESC Algarve 2020","award":["SAICT 39578\/2018"],"award-info":[{"award-number":["SAICT 39578\/2018"]}]},{"name":"Operational Program CRESC Algarve 2020","award":["SAICT 72581\/2020"],"award-info":[{"award-number":["SAICT 72581\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/en14185852","type":"journal-article","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T21:38:12Z","timestamp":1631828292000},"page":"5852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5904-2166","authenticated-orcid":false,"given":"Karol","family":"Bot","sequence":"first","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6078-6813","authenticated-orcid":false,"given":"Inoussa","family":"Laouali","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez 1049-001, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-8666","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Ruano","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0014-9257","authenticated-orcid":false,"given":"Maria da Gra\u00e7a","family":"Ruano","sequence":"additional","affiliation":[{"name":"Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal"},{"name":"CISUC, University of Coimbra, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119271","DOI":"10.1109\/ACCESS.2020.3005244","article-title":"Home energy management system concepts, configurations, and technologies for the smart grid","volume":"8","author":"Zafar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Herczeg, M., McKinnon, D., Milos, L., Bakas, I., Klaassens, E., Svatikova, K., and Widerberg, O. (2014). Resource Efficiency in the Building Sector, CopenHagen research Institute."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khan, Z.A., Ullah, A., Ullah, W., Rho, S., Lee, M., and Baik, S.W. (2020). Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy. Appl. Sci., 10.","DOI":"10.3390\/app10238634"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Atef, S., Ismail, N., and Eltawil, A.B. (2021). A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction. Adv. Build. Energy Res., 1\u201319.","DOI":"10.1080\/17512549.2021.1873183"},{"key":"ref_5","first-page":"1","article-title":"Distributed Machine Learning on Smart-Gateway Network toward Real-Time Smart-Grid Energy Management with Behavior Cognition","volume":"23","author":"Huang","year":"2018","journal-title":"ACM Trans. Des. Autom. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101692","DOI":"10.1016\/j.jobe.2020.101692","article-title":"A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis","volume":"33","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1109\/TPWRS.2017.2746261","article-title":"Optimal Placement and Sizing of Distributed Battery Storage in Low Voltage Grids Using Receding Horizon Control Strategies","volume":"33","author":"Fortenbacher","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.renene.2014.06.046","article-title":"Modeling the impact of integrating solar thermal systems and heat pumps for domestic hot water in electric systems\u2014The case study of Corvo Island","volume":"72","author":"Neves","year":"2014","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"172524","DOI":"10.1109\/ACCESS.2020.3024901","article-title":"Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast","volume":"8","author":"Hossain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110118","DOI":"10.1016\/j.rser.2020.110118","article-title":"Energy management strategies based on hybrid automata for islanded microgrids with renewable sources, batteries and hydrogen","volume":"134","author":"Kafetzis","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Basmadjian, R. (2020). Optimized charging of pv-batteries for households using real-time pricing scheme: A model and heuristics-based implementation. Electronics, 9.","DOI":"10.3390\/electronics9010113"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shakeri, M., Pasupuleti, J., Amin, N., Rokonuzzaman, M., Low, F.W., Yaw, C.T., Asim, N., Samsudin, N.A., Tiong, S.K., and Hen, C.K. (2020). An overview of the building energy management system considering the demand response programs, smart strategies and smart grid. Energies, 13.","DOI":"10.3390\/en13133299"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117149","DOI":"10.1016\/j.apenergy.2021.117149","article-title":"Community-scale interaction of energy efficiency and demand flexibility in residential buildings","volume":"298","author":"Munankarmi","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114103","DOI":"10.1016\/j.enconman.2021.114103","article-title":"A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique","volume":"237","author":"Shivam","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116289","DOI":"10.1016\/j.apenergy.2020.116289","article-title":"An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic","volume":"283","author":"Wang","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e12713","DOI":"10.1002\/2050-7038.12713","article-title":"Optimal energy management of residential battery storage under uncertainty","volume":"31","author":"Su","year":"2021","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115990","DOI":"10.1016\/j.apenergy.2020.115990","article-title":"A dynamic energy management system using smart metering","volume":"280","author":"Mbungu","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115661","DOI":"10.1016\/j.apenergy.2020.115661","article-title":"An optimal home energy management system for modulating heat pumps and photovoltaic systems","volume":"278","author":"Langer","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Narayanan, M., de Lima, A.F., Dantas, A., and Commerell, W. (2020). Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System. Energies, 13.","DOI":"10.3390\/en13215761"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1049\/iet-stg.2020.0090","article-title":"Comparison of economic model predictive control and rule-based control for residential energy storage systems","volume":"3","author":"Banfield","year":"2020","journal-title":"IET Smart Grid"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1049\/iet-stg.2019.0196","article-title":"Reinforcement learning for control of flexibility providers in a residential microgrid","volume":"3","author":"Mbuwir","year":"2020","journal-title":"IET Smart Grid"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kersic, M., Bocklisch, T., Bottiger, M., and Gerlach, L. (2020). Coordination Mechanism for PV Battery Systems with Local Optimizing Energy Management. Energies, 13.","DOI":"10.3390\/en13030611"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TII.2020.2971530","article-title":"Predictive Home Energy Management System With Photovoltaic Array, Heat Pump, and Plug-In Electric Vehicle","volume":"17","author":"Yousefi","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Galvan, E., Mandal, P., Chakraborty, S., and Senjyu, T. (2019). Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids. Sustainability, 11.","DOI":"10.3390\/su11195436"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.apenergy.2019.01.097","article-title":"Economic model predictive control of combined thermal and electric residential building energy systems","volume":"240","author":"Kuboth","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jprocont.2018.11.003","article-title":"A self-interested distributed economic model predictive control approach to battery energy storage networks","volume":"73","author":"Wang","year":"2019","journal-title":"J. Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.solener.2017.12.022","article-title":"Optimal operation of hybrid PV-battery system considering grid scheduled blackouts and battery lifetime","volume":"161","author":"Alramlawi","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.apenergy.2017.06.047","article-title":"A new combined control algorithm for PV-CHP hybrid systems","volume":"210","author":"Kneiske","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"55234","DOI":"10.1109\/ACCESS.2018.2872788","article-title":"Optimal Planning of Multiple Distributed Generating Units and Storage in Active Distribution Networks","volume":"6","author":"Khalid","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.renene.2016.05.006","article-title":"Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage","volume":"100","author":"Zhang","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101186","DOI":"10.1016\/j.est.2019.101186","article-title":"Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings","volume":"28","author":"Baniasadi","year":"2020","journal-title":"J. Energy Storage"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"118456","DOI":"10.1016\/j.energy.2020.118456","article-title":"Centralized model predictive control strategy for thermal comfort and residential energy management","volume":"212","author":"Seal","year":"2020","journal-title":"Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1016\/j.apenergy.2017.08.166","article-title":"Foresee: A user-centric home energy management system for energy efficiency and demand response","volume":"205","author":"Jin","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"109444","DOI":"10.1016\/j.enbuild.2019.109444","article-title":"Experimental short-term investigation of model predictive heat pump control in residential buildings","volume":"204","author":"Kuboth","year":"2019","journal-title":"Energy Build."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ruano, A., Silva, S., Duarte, H., and Ferreira, P.M. (2018). Wireless Sensors and IoT Platform for Intelligent HVAC Control. Appl. Sci., 8.","DOI":"10.3390\/app8030370"},{"key":"ref_36","unstructured":"Gon\u00e7alves, J.A., Braz-C\u00e9sar, M., and Coelho, J.P. (2021). Home Energy Management System in an Algarve residence. First results. CONTROLO 2020: Proceedings of the 14th APCA International Conference on Automatic Control and Soft Computing, Springer Science and Business Media Deutschland GmbH. Lecture Notes in Electrical Engineering 695."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101","DOI":"10.2174\/9781681088327121060006","article-title":"The Impact of Occupants in Thermal Comfort and Energy Efficiency in Buildings","volume":"Volume 6","author":"Ruano","year":"2021","journal-title":"Occupant Behaviour in Buildings: Advances and Challenges"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"31005","DOI":"10.3390\/s151229841","article-title":"An Intelligent Weather Station","volume":"15","author":"Mestre","year":"2015","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bot, K., Ruano, A., and Ruano, M.d.G. (2021). Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems. Inventions, 6.","DOI":"10.3390\/inventions6010012"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0005-1098(87)90087-2","article-title":"Generalized predictive control\u2014Part I. the basic algorithm","volume":"23","author":"Clarke","year":"1987","journal-title":"Automatica"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ferreira, P.M. (2007). Application of Computational Intelligence Methods to Greenhouse Environmental Control. [Ph.D. Thesis, Algarve University].","DOI":"10.1109\/IJCNN.2008.4634310"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1016\/S0967-0661(97)00136-6","article-title":"Fuzzy predictive control applied to an air-conditioning system","volume":"5","author":"Sousa","year":"1997","journal-title":"Control. Eng. Pract."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"236","DOI":"10.3182\/20120403-3-DE-3010.00085","article-title":"Model based predictive control of HVAC systems for human thermal comfort and energy consumption minimisation","volume":"45","author":"Ferreira","year":"2012","journal-title":"IFAC Proc. Vol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.enbuild.2016.03.043","article-title":"The IMBPC HVAC system: A complete MBPC solution for existing HVAC systems","volume":"120","author":"Ruano","year":"2016","journal-title":"Energy Build."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.rser.2012.12.014","article-title":"A review of sensitivity analysis methods in building energy analysis","volume":"20","author":"Tian","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1111\/0272-4332.00039","article-title":"Identification and review of sensitivity analysis methods","volume":"22","author":"Patil","year":"2002","journal-title":"Risk Anal."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/TCST.2013.2295737","article-title":"A Model Predictive Control Approach to Microgrid Operation Optimization","volume":"22","author":"Parisio","year":"2014","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_48","first-page":"524","article-title":"Model predictive control of a hybrid heat pump system and impact of the prediction horizon on cost-saving potential and optimal storage capacity","volume":"148","author":"Conti","year":"2018","journal-title":"Appl. Therm. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8225","DOI":"10.1016\/j.ifacol.2020.12.1985","article-title":"Forecasting Electricity Demand in Households using MOGA-designed Artificial Neural Networks","volume":"53","author":"Bot","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"ref_50","first-page":"313","article-title":"Forecasting Electricity Consumption in Residential Buildings for Home Energy Management Systems","volume":"Volume 1237","author":"Lesot","year":"2020","journal-title":"Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/14\/18\/5852\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:31Z","timestamp":1760166031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/14\/18\/5852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,16]]},"references-count":50,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["en14185852"],"URL":"https:\/\/doi.org\/10.3390\/en14185852","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,16]]}}}