At present

the prediction methods based on statistical analysis and machine learning cannot simultaneously consider the time series and nonlinear characteristics of load data. This paper proposes a short-term power load forecasting method based on GRU-NN model. The method is based on the deep learning idea to deal with different types of load influencing factors

and introduces the gated recurrent unit(GRU)network to process the historical load sequence with time series characteristics. A model is developed to learn the internal dynamic change law of the load data

and its output and other external influence factors(weather

day type)are merged into new input features. The deep neural network is used to process the data. The internal relationship between the characteristics and load changes is analyzed

and the load forecasting is finally completed. Taking the public data set provided by a public utility department in the United States and the load data of a certain region in China as practical examples

the forecasting accuracy of the proposed method is 97. 30% and 97. 12%

respectively. The proposed method is compared with long short-term memory neural network

multi-layer perceptron and GRU neural network

the experimental results show that the proposed method has higher forecasting accuracy and faster forecasting speed.