面向功能分区的大型商场建筑冷负荷预测方法

针对大型商场面向建筑整体冷负荷预测不能为商场各区域按需供冷提供合理控制策略的问题，通过研究商场不同区域冷负荷特点，采用灰色关联度分析法筛选影响商场不同区域冷负荷的关键影响因素，针对实际情况中各输入特征对冷负荷影响程度的不稳定性，提出了基于双重注意力机制和LSTM的短期分区冷负荷预测模型。LSTM网络充分考虑空调冷负荷与相关特征变量之间的非线性关系，特征注意力自主分析历史信息和输入变量之间的关系，提取重要特征，时序注意力选取LSTM网络关键时刻的历史信息，提升较长时间段预测效果的稳定性。以西安某大型商场建筑的冷负荷数据集为实验数据，实验结果表明所提模型相比于LSTM模型、CNN-LSTM模型和Attention-LSTM模型，误差指标MAPE和RMSE均有显著降低，R2明显增加且稳定0.99以上，具有较好的泛化能力和较强的稳定性。

A cold load prediction method of shopping malls oriented to functional zoning

Current cooling load prediction method of overall buildings for large-scale shopping malls cannot provide a reasonable control strategy for demands of various areas of the shopping mall. By studying the characteristics of cooling load in different areas of shopping malls, the key influencing factors of cooling load in different areas of shopping malls were screened by using grey relational degree analysis method. To solve the instability of the influence degree of each input variable on cooling load in actual situation, a short-term zoned cooling load prediction model based on double attention mechanism and LSTM was proposed. LSTM network fully considers the nonlinear relationship between air-conditioning cooling load and related characteristic variables. Feature attention analyzes the relationship between historical information and input variables autonomously to extract important features. Sequential attention selects historical information at critical moments of LSTM network to improve the stability of long-term prediction effects. The experimental results show that compared with LSTM model, CNN-LSTM model and attention-LSTM model, the error indexes MAPE and RMSE of the proposed model decrease significantly, and its R2 increases significantly and remains stable above 0.99, indicating good generalization ability and strong stability.