The setting of Hyper parameter.

Accurate prediction of chiller energy consumption is crucial for reducing building energy consumption. In this study, an innovative dual-branch network architecture DNTB (A Dual-Branch Network Model Based on Transformer and Bi-LSTM for Energy Consumption Prediction in Building Chiller Systems) was proposed to address the problems of insufficient long-term dependency modeling and noise sensitivity in current prediction models. The research goal is to develop a prediction model that can simultaneously process temporal features and global dependencies. The basic principle is to utilize the complementary characteristics of Transformer and Bi-LSTM. Transformer is sensitive to data noise and Bi-LSTM is weak in capturing long-term sequence information. It can better capture the temporal information of chiller energy consumption data and well model the relationship between variables such as chilled water, building load, chiller temperature, humidity, dew point and chiller energy consumption. In order to prove the effectiveness and generalization ability of the model, experiments were carried out on long-term and short-term tasks of chiller energy consumption prediction. The long-term prediction results had MSE (mean absolute error) of 0.0051, RMSE (mean square error) of 0.0605, and R2 (coefficient of determination) of 0.8031. The short-term prediction results had MSE of 0.0080, RMSE of 0.0738, and R2 of 0.6717. The experimental results indicate that DNTB performs excellently in both long-term and short-term chiller energy consumption prediction, making it a robust framework for chiller energy consumption prediction. The introduction of DNTB enriches the diversity of empirical model algorithms.

准确预测冷水机组能耗对于降低建筑能耗至关重要。本研究提出一种创新双分支网络架构DNTB——基于Transformer与双向长短期记忆网络（Bi-LSTM）的建筑冷水机组系统能耗预测双分支网络模型，以解决当前预测模型存在的长期依赖建模不足与噪声敏感性问题。本研究的目标是开发可同时处理时序特征与全局依赖关系的预测模型，其核心原理在于利用Transformer与Bi-LSTM的互补特性：Transformer对数据噪声较为敏感，而Bi-LSTM在捕获长期序列信息方面能力较弱，二者结合可更好地捕捉冷水机组能耗数据的时序信息，并有效建模冷水、建筑负荷、机组温度、湿度、露点等变量与冷水机组能耗之间的关联关系。为验证该模型的有效性与泛化能力，本研究在冷水机组能耗预测的长短期任务上开展实验。实验结果显示，长期预测场景下，模型的均方误差（Mean Squared Error，MSE）为0.0051、均方根误差（Root Mean Squared Error，RMSE）为0.0605、决定系数（Coefficient of Determination，R²）为0.8031；短期预测场景下，模型的MSE为0.0080、RMSE为0.0738、R²为0.6717。实验结果表明，DNTB在长短期冷水机组能耗预测任务中均表现优异，是一种鲁棒性较强的冷水机组能耗预测框架。该模型的提出丰富了能耗预测领域经验模型算法的多样性。