Open Access

ARTICLE

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Pingping Xia^{1, *}, Aihua Xu², Tong Lian¹

1 Department of Information Engineering, Jiangsu Maritime Institute, Nanjing, 211170, China.
2 Nanjing Institute of Science and Technology Information, Nanjing, 210018, China.

* Corresponding Author: Pingping Xia. Email: .

Journal of Quantum Computing 2020, 2(1), 25-32. https://doi.org/10.32604/jqc.2019.09232

Received 25 November 2019; Accepted 30 November 2019; Issue published 28 May 2020

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.

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