Open AccessHousehold Electricity Load Forecasting Based on Pearson Correlation Coefficient Clustering and Convolutional Neural Network
Author(s) -
Minghao Xie,
Chengwei Chai,
Huang Guo,
Minghao Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/2/022012
Subject(s) - pearson product moment correlation coefficient , electricity , cluster analysis , correlation coefficient , euclidean distance , computer science , consumption (sociology) , index (typography) , artificial neural network , econometrics , statistics , artificial intelligence , mathematics , machine learning , engineering , social science , electrical engineering , sociology , world wide web
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.