基于小波变换和长短期记忆神经网络的电力负荷预测

电力系统需要保持发电功率与用电负荷的即时平衡，而电力负荷具有非线性、时变性和不确定性等特点。针对此问题，考虑天气与日期类型的影响，构建小波变换（wavelet transform，WT）和长短期记忆（long short-term memory,LSTM）神经网络组合预测模型，对电力负荷进行短期电力负荷预测。首先，用小波变换对数据集进行特征提取、信号去噪，消除数据的波动性；其次，将预处理后的数据利用LSTM进行训练，将输出结果进行序列重构；最后，进行负荷预测，WT-LSTM组合预测模型分别与BP神经网络预测模型和LSTM预测模型进行对比数据。结果表明，WT-LSTM神经网络组合预测模型的预测效果最好,有效地提高了预测精度。

Power load forecasting based on wavelet transform and long short-term memory neural network

The power system requires an immediate balance between the generated power and the electricity load,which is characterized by non-linearity,time variability,and uncertainty.To address this issue,this paper proposes a combined forecasting model that integrates wavelet transform(WT)and long short-term memory(LSTM)neural networks,considering the impact of weather and date types for short-term power load forecasting.Initially,the wavelet transform is employed for feature extraction signal denoising to reduce data volatility.Then,the preprocessed data is trained using an LSTM network,and the output results undergo sequence reconstruction for the final load forecast.Finally,the data of WT-LSTM combined forcasting model is seperately compared with that of the BP neural network and LSTM model.The results show that the WT-LSTM neural network combined prediction model has the superior predictive performance,significantly enhancing forecasting precision.