Development of Artificial Intelligence in Green Buildings and Renewable Energy

Dear Colleagues,

Globally, buildings account for about 40% of energy consumption and more than 30% of CO₂ emissions. Meanwhile, renewable energy sources such as solar and wind energy have become an attractive alternative to fossil fuels. The rational utilization of renewable energy in modern buildings not only reduces their burden on the environment, but also promotes their energy efficiency. Research on such a cross-disciplinary area is of great significance for "carbon peak and carbon neutrality". Recently, the rapid development of artificial intelligence (AI) technologies, such as deep learning and reinforcement learning, has meant that their use in green buildings and renewable energy is still on the rise. For example, reinforcement learning can obtain an optimal decision-making policy to control the building environment without knowing the building’s thermal dynamics models. Deep learning models outperform traditional machine leaning models in predicting the complex and nonlinear behaviors that exist in solar/wind power generation. Intelligent optimization helps us to arrange solar panels sensibly to achieve an ideal solar power supply in green buildings.

This Special Issue aims to collect scientific contributions that present the use of advanced AI technologies in green buildings and renewable energy, and on subjects including, but not limited to, renewable energy forecasting, renewable energy’s integration into grids, renewable energy resource assessment, renewable energy in green buildings, energy-efficient buildings, building energy consumption prediction, thermal comfort modelling, and the control and optimization of building environments or related areas.

Dr. Wenping Xue
Prof. Dr. Kangji Li
Guest Editors

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• artificial intelligence
• renewable energy forecasting
• renewable energy’s integration into grids
• renewable energy in green buildings
• building energy consumption prediction
• building environment control and optimization
• thermal comfort modelling
• deep learning
• reinforcement learning
• intelligent optimization