Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-161
Energy Optimization Approach for Smart Buildings Considering Supply and Demand Uncertainties
1 Lecturer,
Applied Technology College of Dalian Ocean University, Dalian, 116300,
China.
2 Lecturer, Applied Technology
College of Dalian Ocean University, Dalian, 116300, China, E-mail:
duanzhui@outlook.com (corresponding author).
Engineering Management
Received August 13, 2025; revised September 28, 2025; accepted September 29, 2025
Available online March 7, 2026
Abstract: Energy optimization in intelligent buildings is a multi-objective decision-making problem that requires the consideration of uncertainties on both supply and demand sides. However, existing optimization strategies often consider a single indicator in isolation and lack collaborative system optimization, resulting in poor overall performance. To address this issue, this study proposes an intelligent building energy optimization model based on deep reinforcement learning and multi-criteria decision-making. This model integrates Long Short-Term Memory Transformer network for multi-source temporal feature extraction and modeling, uses differential evolution algorithm to optimize Proximal Policy Optimization reinforcement learning strategy, and introduces analytic hierarchy process to allocate weights to competitive indicators such as energy efficiency, economy, comfort, to construct a comprehensive incentive function that guides the intelligent agent to generate optimization strategies that benefit both supply and demand sides in synergy. The results indicate that the proposed model achieves a 40% convergence rate and a 95.5% policy performance achievement rate. The proposed strategy achieves a maximum energy optimization efficiency of 15.8% and a maximum economic benefit improvement of 16.2%. In simulation tests, the proposed strategy achieved a maximum satisfaction rate of 92.1% on the supply side and 95.6% on the demand side. These results demonstrate that the proposed method can effectively address uncertainties from both the supply and demand sides to formulate optimal strategies that consider energy consumption, economic performance, and other key indicators. This method provides a new perspective for smart building energy optimization and promotes the application of deep learning algorithms in diverse decision-making tasks.
Keywords: Smart building; Energy optimization; Supply and demand; Proximal policy optimization; Analytic hierarchy process
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Citation: Liu, X. and Duan, Z. (2026). Energy Optimization Approach for Smart Buildings Considering Supply and Demand Uncertainties. Journal of Engineering, Project, and Production Management, 16(2), 2025-161.
