基于CNN-LSTM-CMA-GRU的多尺度中期负荷预测方法OA

Accurate mid-term power load forecasting is crucial for power dispatch and resource optimization.Addressing the practical need for daily peak/valley load management in power scheduling,medium-term forecasting is studied with daily maximum/minimum load as the prediction granularity.To overcome the error accumulation caused by the decay of coupling relationships between historical loads and multi-dimensional external variables in traditional methods,a deep neural network time-series forecasting approach incorporating a cross multi-head attention(CMA)mechanism is proposed.The model features three innovative designs:first,a dual-branch convolutional neural net-work(CNN)and long short-term memory(LSTM)network are used to extract the local pattern features of the load sequence and the global temporal correlation of auxiliary variables;second,a cross-multi-head attention layer is designed to establish a dynamic weight mapping be-tween historical load and external variables in future periods;finally,a gated recurrent unit(GRU)achieves adaptive fusion of multi-scale features.Experimental results demonstrate that the model achieves high accuracy and ro-bustness in power load forecasting tasks.