Urban Residential Power Load Risk Identification Based on Data Mining

Following the fast urban economic development and the improvement of people living standard, urban residential electricity consumption is increasing quickly. Although the influencing factors of urban residential power demand are complex, objective analysis of risk factors of urban residential load will offer a scientific decision base for the urban power planning and Power Demand-side management. In this paper, related factors with resident living power utility of nine typical cites is chosen based on data mining technology, and association rule mining is finished based on R program language. Then, frequent itemsets of dataset associated with resident living power utility is excavated based on backward algorithm programming. Finally, risk factors of urban residential load can be concluded according to frequent itemsets. This method can avoid subjectivity of determining the risk factors of urban residential load, and also can provide a decision foundation for scientific forecasting of urban residential electricity consumption.