基于多源数据和模型融合的超短期母线负荷预测方法  

Multi-source Data and Hybrid Neural Network Based Ultra-short-term Bus Load Forecasting

作　　者：范士雄 刘幸蔚 於益军 张伟 李立新 FAN Shixiong;LIU Xingwei;YU Yijun;ZHANG Wei;LI Lixin(Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology,(China Electric Power Research Institute),Haidian District,Beijing 100192,China;National Power Dispatching and Control Center,State Grid Corporation of China,Xicheng District,Beijing 100031,China)

机构地区：[1]电力调度自动化技术研究与系统评价北京市重点实验室(中国电力科学研究院有限公司),北京市海淀区100192 [2]国家电网有限公司国家电力调度控制中心,北京市西城区100031

出　　处：《电网技术》2021年第1期243-250,共8页Power System Technology

基　　金：国家重点研发计划项目(2018AAA0101500);国家电网公司总部科技项目“基于潮流数据计算机视觉化图形的电网故障深度辨识技术研究”(5100-201955381A-0-0-00)。

摘　　要：母线负荷预测对于电网调度运行的安全性和在线分析决策的准确性具有重要的意义。为了进一步提高母线负荷预测精度,提出了一种基于多源数据和模型融合的超短期母线负荷预测方法。结合当前电力大数据,首先将历史负荷数据、日期信息以及天气信息等多类型数据作为预测模型的输入特征,并建立基于BP-ANN(back propagation)神经网络和CNN(convolutional neural network)神经网络融合的预测模型。然后采用BP-ANN提取数值类型和类别类型数据的特征向量,与CNN提取图像型数据的特征向量进行融合,通过多层BP-ANN神经网络进行超短期母线负荷的预测。最后,采用我国某地区220kV变电站高压侧的有功负荷历史数据和该地区天气信息进行实例分析。实验结果分析表明,所提方法能够充分有效利用多源数据和模型融合的特点进行超短期母线负荷预测,相较于BP-ANN和CNN单独模型预测具有更高的负荷预测精度。Bus load forecasting plays an important role in terms of the safety of power grid dispatching and operating and the accuracy of online analyzing and decision-making. In order to improve the accuracy of bus load forecasting, this paper proposes an ultra-short-term load forecasting method based on multi-type data and hybrid neural network model. Under the background of the big power data, the multi-source data such as the historical bus load data, the date information and the weather information are firstly taken as the input features, and a predictive model is established based on the fusion of the back propagation(BP-ANN) neural network and the convolutional neural network(CNN). Then, the feature vector extraction from the numeric type and category type data of the BP-ANN is concatenated with that of CNN from the image type data. The combined features are fed to a fully connected multilayer Neural Network(NN) to predict the bus load. In this paper, the historical data of active power load at the high voltage side of a 220 kV substation in a certain area of China and the corresponding weather information are used for example analysis. The analysis of experimental results shows that the proposed method can effectively utilize the characteristics of multi-source data and model fusion to conduct ultra-short-term bus load prediction, and it has higher load prediction accuracy compared to the single model prediction of either BP-ANN or CNN.

关 键 词：母线负荷 BP神经网络 模型融合 超短期负荷预测 卷积神经网络 特征融合 

分 类 号：TM715[电气工程—电力系统及自动化]