摘要
随着智能电网的快速发展,针对电力部门亟需解决的短期电力负荷预测的问题,提出了一种基于栈式自编码和GRU神经网络的短期电力负荷预测方法,方法首先对输入的历史数据,包括电力负荷、天气信息和节假日信息等进行栈式自编码,从而将输入数据进行压缩,然后利用多层GRU构建神经网络,从而预测电力负荷,实例结果表明,将文本提出的电力负荷预测模型能有效预测电力负荷的日变化,与其它常用模型进行比对,预测误差更小,精度更高。
With the rapid development of smart grid, a short-term load forecasting method based on stack self-coding and GRUneural network is proposed. Methods the input historical data, including power load, weather information and holiday information, arecompressed by stack self-coding, and then the neural network is constructed using multi-layer GRU to predict the power load. Theresults show that the proposed power load forecasting model can effectively predict the diurnal variation of power load, and comparedwith other commonly used models, the forecasting error is smaller and the accuracy is higher.
出处
《科技创新与应用》
2018年第33期52-53,57,共3页
Technology Innovation and Application
