A CNN Based Bagging Learning Approach to Short-Term Load Forecasting in Smart Grid

被引:0
作者
Dong, Xishuang [1 ]
Qian, Lijun [1 ]
Huang, Lei [1 ]
机构
[1] Prairie View A&M Univ, Texas A&M Univ Syst, Prairie View, TX 77446 USA
来源
2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | 2017年
关键词
Convolutional Neural Networks; Bagging Learning; Electricity Load Forecasting; Big Data; NEURAL-NETWORK;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Short-term load forecasting in smart grid is key to electricity dispatch scheduling, reliability analysis, and maintenance planning for the generators. In this paper, we present a convolutional neural networks (CNN) based bagging model for forecasting hourly loads. We employ CNN to train forecasting models on big load data sets. Then, we segment a real industry load data set into many subsets, fine-tune the forecasting models on these subsets to learn weak forecasting models, and assemble these weak forecasting models to conduct a bagging forecasting model, where the learning and assembling procedures are implemented on Spark. Specifically, all load samples in those data sets are reorganized as images with respect to similarities between relations of pixels in images and those of features in load samples. Experimental results indicate the effectiveness of the proposed method.
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页数:6
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