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Load forecasting is an important content of planning and operating power system. It is the prerequisite to ensure the reliable power supply and economic operation. In this paper, an improved method of short-term load forecasting for load data of two different regions is proposed. Firstly, we analyze the relationship between weather factors and load, and then the greatest impact on load of weather factors are selected. The Elman neural network is used to predict unknown one-week load data taking into account without whether factors situation and whether factors situation. In the predicting situation of considering whether factors, the multi-weather factors are integrated with the temperature and humidity index , which are used as the neural network input training samples. The prediction result is good.

An Improved Method of Power System Short Term Load Forecasting Based on Neural Network