Sustainable Smart City Building-energy Management Based on Reinforcement Learning and Sales of ESS Power

• Dae-Kug Lee,
• Seok-Ho Yoon,
• Jae-Hyeok Kwak,
• Choong-Ho Cho,
• Dong-Hoon Lee,

Vol. 17, No. 4, April 30, 2023
10.3837/tiis.2023.04.005, Download Paper (Free):

• AI
• Big data
• Building-energy Management
• Energy Storage System
• Reinforcement Learning
• Renewable energy
• Smart City

Abstract

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

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