Credit risk evaluation in power market with random forest

This paper proposes a new method for credit risk evaluation in a power market. The proposed method is based on Random Forest of data mining. In recent years, the power market becomes more deregulated and competitive. The power market players are concerned with both profit maximization and risk minimization. As a management strategy, a risk index is required to evaluate the worth of the business partner. In this paper, a new method is proposed to evaluate the credit risk with Random Forest that makes use of ensemble learning for the decision tree. It is one of efficient data mining technique in clustering data and extraction rules from data. The proposed method is successfully applied to financial data of energy utilities in the market.