一个新的双重时间序列网络用于预测建筑能耗,预测能源消耗
Zhixin Sun1, Han Cui1, Xiangxiang Mei2
1College of Safety Engineering and Emergency Management, Nantong Institute of Technology, Nantong, Jiangsu, China.
PloS one
|June 26, 2025
概括
双子时间序列网络 (T2SNET) 通过增强时间相关性提取和融合多源数据,改善建筑能耗预测. 这种新的方法为优化能源管理系统提供了强大的解决方案.
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
- 时间序列预测时间序列预测
背景情况:
- 准确的建筑能耗预测对于高效的能源管理至关重要.
- 目前的方法在时间相关性,预测准确性,时间嵌和多源数据融合方面扎.
- 现有的模型往往无法充分利用时间序列分解和自适应数据集成的潜力.
研究的目的:
- 为了解决当前建筑能源预测模型的局限性.
- 提出一种先进的模型,增强时间相关性提取和预测准确度.
- 有效地整合多来源数据,包括能源消耗和气象信息.
主要方法:
- 双胞胎时间序列网络 (T2SNET) 的开发.
- 时间嵌入层和时间卷积网络 (TCN) 的集成,用于从完整集体实证模式分解与自适应噪声 (CEEMDAN) 中提取模式.
- 实现适应性聚变门,将能源消耗和气象数据结合起来.
主要成果:
- 在各种建筑类型 (宿舍,办公室,教室) 中,T2SNET与基线方法相比显著改进.
- 在大学课堂数据集中,与CEEMDAN-RF-LSTM相比,T2SNET实现了4.56%的平均绝对误差 (MAE),9.45%的根平均平方误差 (RMSE) 和3.16%的平均绝对百分比误差 (MAPE) 的降低.
- 该模型有效地提取时间模式,并融合多源数据,从而获得卓越的预测性能.
结论:
- T2SNET为建筑能源消耗预测提供了强大而有效的解决方案.
- 拟议的方法克服了时间相关性和数据融合方面的关键挑战.
- 这些发现支持采用T2SNET用于先进的能源管理系统.
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