An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic
Author:
Papaioannou Ioannis12, Dimara Asimina13ORCID, Korkas Christos12ORCID, Michailidis Iakovos12ORCID, Papaioannou Alexios14, Anagnostopoulos Christos-Nikolaos3, Kosmatopoulos Elias12, Krinidis Stelios14, Tzovaras Dimitrios1
Affiliation:
1. Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece 2. Electrical and Computer Engineering Department, Democritus University of Thrace, 67100 Xanthi, Greece 3. Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece 4. Management Science and Technology Department, International Hellenic University (IHU), 65404 Kavala, Greece
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.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference39 articles.
1. US EIA (2018). Global Energy & CO2 Status Report: The Latest Trends in Energy and Emissions in 2018. 2. Korkas, C., Dimara, A., Michailidis, I., Krinidis, S., Marin-Perez, R., Martínez García, A.I., Skarmeta, A., Kitsikoudis, K., Kosmatopoulos, E., and Anagnostopoulos, C.N. (2022). Integration and Verification of PLUG-N-HARVEST ICT Platform for Intelligent Management of Buildings. Energies, 15. 3. Papaioannou, A., Dimara, A., Michailidis, I., Stefanopoulou, A., Karatzinis, G., Krinidis, S., Anagnostopoulos, C.N., Kosmatopoulos, E., Ioannidis, D., and Tzovaras, D. (2023, January 14–17). Self-protection of IoT Gateways Against Breakdowns and Failures Enabling Automated Sensing and Control. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, León, Spain. 4. Artificial intelligence for smart renewable energy sector in europe—Smart energy infrastructures for next generation smart cities;Lytras;IEEE Access,2020 5. An overview of machine learning applications for smart buildings;Alanne;Sustain. Cities Soc.,2022
|
|