电力系统自动化2019，Vol.43Issue(5)：53-58,6.DOI:10.7500/AEPS20180620003
基于GRU-NN模型的短期负荷预测方法
Short-term Load Forecasting Method Based on GRU-NN Model
摘要
Abstract
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.关键词
电力系统/短期负荷预测/门控循环单元/深度神经网络Key words
power system/short-term load forecasting/gated recurrent unit (GRU)/deep neural network引用本文复制引用
王增平,赵兵,纪维佳,高欣,李晓兵..基于GRU-NN模型的短期负荷预测方法[J].电力系统自动化,2019,43(5):53-58,6.基金项目
国家重点研发计划资助项目(2016YFF0201201) （2016YFF0201201）
This work is supported by National Key R&D Program of China (No. 2016YFF0201201). （No. 2016YFF0201201）
