摘要
目前基于统计分析和机器学习的预测方法难以同时兼顾负荷数据的时序性和非线性特点。文中提出了一种基于GRU-NN模型的短期电力负荷预测方法。该方法基于深度学习思想处理不同类型的负荷影响因素,引入门控循环单元(GRU)网络处理具有时序性特点的历史负荷序列,建模学习负荷数据内部动态变化规律,其输出结果与其他外部影响因素(天气、日类型等)融合为新的输入特征,使用深度神经网络进行处理,整体分析特征与负荷变化的内在联系,最后完成负荷预测。以美国某公共事业部门提供的公开数据集和中国某地区的负荷数据作为实际算例,该方法预测精度分别达到了97.30%和97.12%,并与长短期记忆神经网络、多层感知机以及GRU神经网络方法进行对比,实验结果表明所提方法具有更高的预测精度和更快的预测速度。
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.
作者
王增平
赵兵
纪维佳
高欣
李晓兵
WANG Zengping;ZHAO Bing;JI Weijia;GAO Xin;LI Xiaobing(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;China Electric Power Research Institute,Beijing 100192,China;School of Automation,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2019年第5期53-62,共10页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2016YFF0201201)~~
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
power system
short-term load forecasting
gated recurrent unit(GRU)
deep neural network
