研究中长期电力负荷精确预测问题,针对基于传统BP神经网络算法建立的电力负荷预测模型中存在的局部极小值、预测精度低、收敛速度慢等问题,提出了一种改进的BP神经网络电力负荷预测算法,利用自适应调整学习率和批处理样本训练方式,提高神经网络本身的收敛速度和精度。最后以河南省某地区电力数据为例验证所提模型和算法的可行性和合理性。 In order to solve the problems of the local minimum in the power load forecasting model based on the traditional BP neural network algorithm, the prediction accuracy is low and the convergence speed is slow, an improved BP neural network Load forecasting algorithm, adaptive adjustment of the learning rate and batch training methods to improve the neural network convergence speed and accuracy. Finally, taking the electric power data in a certain area of ​​Henan Province as an example, the feasibility and rationality of the proposed models and algorithms are verified.