This paper introduces the methods and the steps of predicting the power load by the BP neural network with cluster optimization in batch processing time series. Through the preconditioning of historical data, the setting of the initial clustering center and the determination of the optimal number of clusters, a clustering prediction model of the load curve is established based on the clustering results of the historical data and the relevant parameters such as the temperature, the humidity, the air pressure, the wind speed and the time (the current week). The results show that with the clustering algorithm, the related factors and the BP network adaptive rate can be comprehensively considered, while the training speed is improved, to obtain more accurate prediction results.

cluster analysis  /  neural networks  /  power load forecasting  /  data mining