基于WD-LSSVM-LSTM模型的短期电力负荷预测

Short-term load forecasting based on WD-LSSVM-LSTM model

为了提高短期负荷预测精度,文中提出一种基于小波分析、粒子群优化(PSO)算法、最小二乘支持向量机(LSSVM)和长短时记忆网络(LSTM)的预测模型。该方法通过对用电负荷进行小波分解和重构得到与原始数据长度相同的分量,对低频分量建立LSSVM预测模型并利用PSO算法找出最优参数,对高频分量建立LSTM预测模型,将各分量预测结果组合实现最终的负荷预测。实验结果表明,该模型预测精度优于传统LSSVM模型、BP神经网络模型和WD-LSSVM模型,验证了其可行性。

In order to improve the accuracy of short-term load forecasting,a forecasting model based on wavelet analysis,particle swarm optimization(PSO)algorithm,least squares support vector machine(LSSVM)and long-short term memory network(LSTM)was proposed in this paper.In this method,the same length component of the original data was obtained by wavelet decomposition and reconstruction of the power load.The LSSVM prediction model was established for the low-frequency component and the optimal parameters were found by PSO algorithm.The LSTM prediction model was established for the high-frequency component and the final load prediction was realized by combining the prediction results of each component.The experimental results showed that the prediction accuracy of the proposed model is better than that of the traditional LSSVM model,BP neural network model and PSO-LSSVM model,and its feasibility is verified.