考虑温湿指数与耦合特征的综合能源负荷短期预测

Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics

针对综合能源负荷易受气象因素影响及其异质能量耦合特性所导致的预测建模复杂、准确性不高等问题,提出了一种考虑温湿指数与耦合特征的负荷短期预测模型。首先,在深入挖掘多元负荷耦合特征的基础上,结合温湿指数构造计及多因素影响的输入变量;然后,利用核主成分分析(KPCA)法在确保信息有效的前提下完成对预测输入空间的降维处理,并基于门控循环单元(GRU)神经网络进行预测建模,进一步引入Attention机制实现重要特征的差异化提取;最后,选取某实际系统电、冷负荷数据进行仿真。仿真结果表明,基于KPCA-GRU-Attention模型的电、冷负荷短期预测结果的均方根误差和平均绝对百分误差分别为1025 kW,2.7%和2167 kW,2.9%,准确性得到了显著提升。所提方法能够在考虑多因素影响的基础上有效提高综合能源负荷的短期预测精度,实现了对用能需求的精准感知。

To address the vulnerability of integrated energy load prediction to meteorological factors and the complexity and low accuracy of prediction models caused the coupling characteristics of heterogeneous energy,a short-term load forecasting model considering temperature-humidity index and coupling characteristics is proposed.Excavating the coupling characteristics of multiple loads,the input variables considering the influence of temperature-humidity index and multiple factors are constructed.Ensuring that the information is valid,kernel principal component analysis(KPCA)is used to complete the dimensional reduction of prediction input space,and the prediction model is built based on gated recurrent unit(GRU)neural network.Attention mechanism is introduced into the model to extract important differentiated features.Finally,the electric and cooling load data of a practical system are selected for simulation,and the results show that the root mean square errors and mean absolute percentage errors of the electric and cooling load predicted by KPCAGRU-Attention model are 1025 kW,2.7%and 2167 kW,2.9%,respectively.The accuracy has been significantly improved.The proposed model effectively improves the short-term prediction accuracy of integrated energy loads by considering the influence of multiple factors,realizing the accurate perception on energy demand.