General Topic of Hunan Social Science Achievement Review Committee in 2024 [XSP24YBC500]; Key Laboratory of Key Technologies of Digital Urban-Rural Spatial Planning of Hunan Province [2018TP1042]
机构署名：
本校为第一机构
院系归属：
建筑与城市规划学院
摘要：
Energy consumption forecasting in green buildings remains challenging due to complex climate-building interactions and temporal dependencies in energy usage patterns. Existing prediction models often fail to capture long-term dependencies and adapt to diverse climatic conditions, limiting their practical applicability. This study presents an integrated forecasting framework that combines sequence-to-sequence (Seq2Seq) architecture with reinforcement learning and transfer learning techniques. The framework employs long short-term memory (LSTM) networks enhanced with attention mechanisms to mode...
