@Article{jqc.2019.09232,
AUTHOR = {Pingping Xia, Aihua Xu, Tong Lian},
TITLE = {Analysis and Prediction of Regional Electricity Consumption  Based on BP Neural Network},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {25--32},
URL = {http://www.techscience.com/jqc/v2n1/39236},
ISSN = {2579-0145},
ABSTRACT = {Electricity consumption forecasting is one of the most important tasks for 
power system workers, and plays an important role in regional power systems. Due to the 
difference in the trend of power load and the past in the new normal, the influencing 
factors are more diversified, which makes it more difficult to predict the current 
electricity consumption. In this paper, the grey system theory and BP neural network are 
combined to predict the annual electricity consumption in Jiangsu. According to the 
historical data of annual electricity consumption and the six factors affecting electricity 
consumption, the gray correlation analysis method is used to screen the important factors, 
and three factors with large correlation degree are selected as the input parameters of BP 
neural network. The power forecasting model uses nearly 18 years of data to train and 
validate the model. The results show that the gray correlation analysis and BP neural 
network method have higher accuracy in power consumption prediction, and the 
calculation is more convenient than traditional methods.},
DOI = {10.32604/jqc.2019.09232}
}