AUTHOR=Yan Yongbing , Shao Yan , Wang Dong , Yang Zhe , Ma Haibo , Li Qingjun , Li Peiyi 

TITLE=Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1326525

DOI=10.3389/fenrg.2024.1326525

ISSN=2296-598X

ABSTRACT=In recent years, Northeast China is faced with a severe power shortage situation. Starting from September 2021, "power rationing" events will occur in many places in the three provinces of Northeast China, which will bring inconvenience to people's production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, the pressure of coal and electricity will be further increased. Heilongjiang Province also due to coal capacity control, limited production led to the high price of thermal coal, wind power photovoltaic output fluctuations and the epidemic and other reasons, resulting in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of People's Daily electricity consumption, rational distribution of power generation plans and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of BP(back propagation) neural network prediction model, so that the final prediction results not only have the advantages of BP neural network prediction model and Markov model, Besides, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately.