DNTB:基于变压器和Bi-LSTM的双分支网络模型,用于预测建筑冷却系统中的能源消耗
Andong Chen1, Mingtao Wu2, Cheng Chen3
1Jiangsu Railway Group Rongfa Management Co., Ltd., Nanjing, Jiangsu, China.
PloS one
|October 3, 2025
概括
一个新的双分支网络模型 (DNTB) 通过结合变压器和Bi-LSTM来改善冷却器能耗预测. 该模型有效地捕捉了长期的依赖性,并处理噪音,提高了建筑物的能源效率.
科学领域:
- 建筑能源系统 建筑能源系统
- 工程领域的人工智能
- 预测建模的预测建模.
背景情况:
- 准确的冷却器能源消耗预测对于减少建筑物整体能源消耗至关重要.
- 现有的模型在长期依赖和噪音敏感性方面扎.
- 需要能够处理时间特征和全球依赖关系的模型.
研究的目的:
- 提出创新的双分支网络架构,DNTB (基于变压器和Bi-LSTM的双分支网络模型用于建筑冷却系统中的能源消耗预测).
- 开发一个有效捕获时间信息和冷却器能耗数据的全球依赖性的预测模型.
- 利用变压器和Bi-LSTM的互补优势,克服现有预测模型的局限性.
主要方法:
- 开发了一个双分支网络架构 (DNTB),集成变压器和Bi-LSTM.
- 利用变压器以捕捉全球依赖的能力,Bi-LSTM以其在建模长期序列方面的优势.
- 模拟了变量之间的关系,包括冷却水,建筑负载,冷却器温度,湿度和露点与冷却器能耗.
主要成果:
- 在长期和短期冷却器能耗预测任务中,DNTB表现出色.
- 长期预测实现了0.0051的平均绝对误差 (MSE),0.0605的根平均平方误差 (RMSE) 和0.8031.2的R平方 (R2).
- 短期预测给出了一个MSE的0.0080,RMSE的0.0738,和R2的0.6717.
结论:
- DNTB模型为准确的冷却器能耗预测提供了一个强大的框架.
- 该模型能够处理噪音和长期依赖性的能力提高了其概括能力.
- 引入DNTB有助于多样化建筑能源管理的经验模型算法.
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