A Hybrid Control Strategy Combining Reinforcement Learning and MPC-LSTM for Energy Management in Building

Amal Azzi, Meryem Abid, Ayoub Hanif, Hassna Bensag, Mohamed Tabaa (), Hanaa Hachimi and Mohamed Youssfi
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Amal Azzi: Multidisciplinary Laboratory of Research and Innovation (LPRI) Lab, Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Meryem Abid: Multidisciplinary Laboratory of Research and Innovation (LPRI) Lab, Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Ayoub Hanif: Multidisciplinary Laboratory of Research and Innovation (LPRI) Lab, Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Hassna Bensag: Multidisciplinary Laboratory of Research and Innovation (LPRI) Lab, Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Mohamed Tabaa: Multidisciplinary Laboratory of Research and Innovation (LPRI) Lab, Moroccan School of Engineering Sciences (EMSI), Casablanca 20250, Morocco
Hanaa Hachimi: Laboratory of Advanced Systems Engineering (LISA), Ibn Tofail University (UIT), Kenitra 14000, Morocco
Mohamed Youssfi: Computer Science, Artificial Intelligence and Cyber Security Laboratory (2IACS), ENSET, University Hassan II of Casablanca, Mohammedia 28830, Morocco

Energies, 2025, vol. 18, issue 17, 1-22

Abstract: Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit.

Keywords: energy management system; building; LSTM; reinforcement learning; MPC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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