Household Electricity Load Forecasting Based on Pearson Correlation Coefficient Clustering and Convolutional Neural Network. (July 2020)

• Record Type: Journal Article
• Authors: Xie, Minghao; Chai, Chengwei; Guo, Heng; Wang, Minghao
• Is Part Of: Journal of physics. Volume 1601:Number 2(2020)
• Publication Date: 2020-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1601/2/022012
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the development and construction of country, the rapid growth of electricity consumption has caused the problem of undersupply of electricity. It has become a necessarily daily work to forecast the load of the electricity precisely. A new method, new clustering load forecasting method, is used to forecast residential electricity consumption. Distinguished from other methods with Euclidean distance as their evaluation index, however, the method in this paper is defined with the Pearson correlation coefficient. Furthermore, CNN is used in the experiment about residential load forecast. The result indicates that the new method offers more accurate forecasting data than the traditional methods.