Application of Power Big Data in Targeted Poverty Alleviation—Taking Poverty Counties in Jiangxi Province as an Example

Targeted poverty alleviation is an important measure to promote China’s all-round development, but traditional economic surveys and statistics are limited by multiple factors, making it difficult to accurately identify poor targets in a timely manner. The development of power big data provides the possibility to use energy consumption data to locate and identify poor areas. Therefore, this article takes Jiangxi Province as an example to analyze 23 regions that have been classified as poverty-stricken counties (8 counties have been separated from the list of impoverished counties). First, panel data regression is performed to prove that electricity sales can be used to analyze and predict regional economic development. Then, using decision tree ID3 algorithm and four neural network algorithms to classify and forecast poor and non-poor counties, it is found that ID3 algorithm has good fitting and prediction accuracy. Therefore, power big data can be applied to the work of targeted poverty alleviation, and has a good prospect.