在建筑物中使用LSTM和SVR进行能源消耗预测,并通过亨利开发的气体溶解率优化改进SVR
Hailu Wan1, Gengqiang Huang2, Ying Huang3,4
1Guangxi Technological College of Machinery and Electricity, Nanning, 530007, Guangxi, China. 63010662004@msu.ac.th.
Scientific reports
|October 31, 2025
概括
这项研究引入了一种混合模型,用于准确预测建筑能源消耗,大大改善了比独立方法的预测. 这种新的方法提高了大型建筑的能源管理和运营效率.
科学领域:
- 建筑能源管理 建筑能源管理
- 能源系统中的人工智能
- 预测方法 预测方法
背景情况:
- 准确的建筑能耗预测对于有效的能源管理,可持续性倡议和运营效率至关重要.
- 现有的预测模型往往难以捕捉复杂的时间动态并有效地优化参数.
- 大规模的建筑能源系统需要强大的预测工具来满足需求响应和资源分配.
研究的目的:
- 开发和验证用于建筑能耗的新型混合预测模型.
- 整合波形分解,长短期记忆 (LSTM) 网络和支持向量回归 (SVR) 以提高预测准确性.
- 使用开发的亨利气体溶解度优化 (DHGSO) 算法优化模型参数.
主要方法:
- 混合模型结合波纹分解用于特征提取,LSTM用于时间模式,SVR用于精细预测.
- 使用开发的亨利气体可溶性优化 (DHGSO) 算法进行的参数优化.
- 在七所大学校园的两年每小时能耗数据上进行了验证.
主要成果:
- 与独立的LSTM和SVR模型相比,拟议的混合模型显示,根平均平方误差 (RMSE) 减少了20%,平均绝对百分比误差 (MAPE) 减少了15%.
- 基于分解的特征工程,深度学习和元启发式优化的集成显著提高了预测准确性.
- 该模型有效地捕获了每小时能耗数据中的复杂时间依赖.
结论:
- 新的混合预测模型为建筑能源消耗预测提供了卓越的准确性.
- 该方法为主动需求响应,预算规划和可再生能源整合提供了有价值的工具.
- 这种方法提高了大型建筑能源管理系统的效率.
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