An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic

Ioannis Papaioannou (), Asimina Dimara, Christos Korkas (), Iakovos Michailidis, Alexios Papaioannou, Christos-Nikolaos Anagnostopoulos, Elias Kosmatopoulos, Stelios Krinidis and Dimitrios Tzovaras
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Ioannis Papaioannou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Asimina Dimara: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Christos Korkas: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Iakovos Michailidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Alexios Papaioannou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Christos-Nikolaos Anagnostopoulos: Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Elias Kosmatopoulos: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Stelios Krinidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Energies, 2024, vol. 17, issue 3, 1-21

Abstract: Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization.

Keywords: reinforcement learning; building controls; smart buildings; occupants well-being; AWAC (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: 2024
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