{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:41:33Z","timestamp":1767994893519,"version":"3.49.0"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Ada Lett."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p>Unlike traditional controllers, Model Predictive Control (MPC) leverages predictive models to predict future system behavior, enabling proactive and efficient decision-making over a defined time horizon. Its effectiveness depends on the accuracy of the models used. Inaccurate predictions can compromise system reliability, reduce stability, and lead to suboptimal control decisions. This work considers the problem of optimizing energy efficiency while ensuring thermal comfort inside buildings, aiming at proposing a suitable MPC strategy in this context. This paper presents preliminary results towards this goal, comparing a gray-box Resistor-Capacitor (RC) model and a black-box Artificial Neural Network (ANN) trained on Pseudo-Random Binary Sequence (PRBS) generated data. Results show that while the RC model is interpretable, the ANN achieves superior long-term accuracy. Observing the Mean Absolute Error (MAE) curves and their standard deviation, we conclude that the reliable prediction horizon, which ensures a prediction error below 1\u00b0C, is only 9.5 hours with the RC model, and increases to more than 24 hours with the ANN model, making it more suitable for MPC applications.<\/jats:p>","DOI":"10.1145\/3784987.3784996","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T15:59:39Z","timestamp":1767974379000},"page":"84-88","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Reliable Predictive Models for Energy-Efficient MPC in Buildings"],"prefix":"10.1145","volume":"45","author":[{"given":"Alexandre","family":"Geraldo","sequence":"first","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, Portugal"}]},{"given":"Ant\u00f3nio","family":"Casimiro","sequence":"additional","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, Portugal"}]},{"given":"Alan Oliveira","family":"de S\u00e1","sequence":"additional","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, Portugal"}]},{"given":"Jos\u00e9","family":"Cec\u00edlio","sequence":"additional","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, Portugal"}]},{"given":"Pedro M.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.112664"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.03.038"},{"key":"e_1_2_1_3_1","first-page":"5","volume-title":"Control and Communication (SCC)","author":"Abobakr S.","year":"2023","unstructured":"S. Abobakr, M. Alosta, M. A. Abdelkefi, A. E. Kaouachi, and L. Sboui, \"Mpc-based efficient energy control and cost estimation of hvac in buildings,\" in 2023 IEEE Third International Conference on Signal, Control and Communication (SCC), pp. 1\u20135, 2023."},{"key":"e_1_2_1_4_1","first-page":"664","volume-title":"Energy and Communication Conference (GPECOM)","author":"Micheneau K.","year":"2024","unstructured":"K. Micheneau and A. Boodi, \"Optimizing comfort and energy efficiency: The impact of model accuracy on multi-objective mpc,\" in 2024 6th Global Power, Energy and Communication Conference (GPECOM), pp. 659\u2013664, 2024."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2012.08.002"},{"key":"e_1_2_1_6_1","first-page":"042","volume-title":"Learning-based model predictive control for smart building thermal management,\" in 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT)","author":"Eini R.","year":"2019","unstructured":"R. Eini and S. Abdelwahed, \"Learning-based model predictive control for smart building thermal management,\" in 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), pp. 038\u2013042, 2019."},{"key":"e_1_2_1_7_1","series-title":"SimBuild Conference","first-page":"180","volume-title":"Gray- and Black-Box Models for Thermal Simulation of Indoor Environment: Teaching Building Case Study,\"","author":"Arendt K.","year":"2018","unstructured":"K. Arendt, M. Jradi, H. Shaker, and C. Veje, \"Comparative Analysis of White-, Gray- and Black-Box Models for Thermal Simulation of Indoor Environment: Teaching Building Case Study,\" vol. 8 of SimBuild Conference, pp. 173\u2013180, ASHRAE\/IBPSA-USA, 2018."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.04.013"},{"key":"e_1_2_1_9_1","article-title":"PySwarms, a research-toolkit for Particle Swarm Optimization in Python","volume":"3","author":"Miranda L. J. V.","year":"2018","unstructured":"L. J. V. Miranda, \"PySwarms, a research-toolkit for Particle Swarm Optimization in Python,\" Journal of Open Source Software, vol. 3, 2018.","journal-title":"Journal of Open Source Software"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"}],"container-title":["ACM SIGAda Ada Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3784987.3784996","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T15:59:42Z","timestamp":1767974382000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3784987.3784996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":10,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.1145\/3784987.3784996"],"URL":"https:\/\/doi.org\/10.1145\/3784987.3784996","relation":{},"ISSN":["1094-3641"],"issn-type":[{"value":"1094-3641","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"2026-01-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}