基于过程神经网络的短期负荷预测

Short-Term Load Forecasting Based on Process Neural Network

针对目前常用方法在解决负荷预测问题时,结果往往难以达到工程要求精度的现状,利用过程神经网络输入为时间函数以及预测精度高的特点,建立了基于过程神经网络的电力系统短期负荷预测模型;给出了模型的结构,基于函数正交基展开的离散数据拟合方法以及模型的学习算法.针对东北某地区电网的日负荷数据,进行了模型训练和负荷预测正确性的研究.结果表明,所建立的预测模型对负荷的预测准确率高,优于BP神经网络负荷预测模型的预测结果.

Conventionally the electric load forecasting can hardly attain a result whose accuracy meets what＇s required. A short-term load forecasting model is therefore developed to .solve the problem, based on the process neural network of which the input is the function of time and the high forecasting accuracy is available. Describes the structure of the model, discrete data fitting method by the expansion of function orthogonal basis and learning algorithm. According to the daily load data of a certain power network in Northeast China, the model training and the accuracy of load forecasting were investigated. The simulation results showed that the load forecasting model based on process neural network is better than on BP neural network.