Who Should Participate in DR Program?Modeling with Machine Learning and Credit Scoring

Abstract

In this study, we consider a residential DR program of incentive-based peak power reduction where invitation for participation can be sent selectively. The selective process can be crucial for improving efficiency of the program for two reasons. First, there are customers who do not change their behavior at all but take rewards due to the natural variations in their life patterns. Second, too many notifications can cause adversarial effects where participants turn off the notification channels or make complaint calls. For the selective process, obviously the process needs to be made as efficient as possible, but it is also essential to maximize the explainability of the selection process such that the operation of the program can be made smooth. To address this problem, we propose a customer participation behavior prediction model considering both accuracy and explainability, where the accuracy advantage of Machine Learning (ML) and the explainability advantage of Credit Scoring (CS) are combined. For the study, data was collected from 15,091 households in Korea for one year in 2016. ML algorithms, with up to 56 features, were studied and showed a fairly high prediction performance (AUROC 0.9576), but they were too complicated to satisfy explainability. A CS method of classing with a scorecard was adopted, where its explainability has been heavily tested and proven in the financial sector already. Direct adoption of general CS, however, does not guarantee an acceptable accuracy performance because energy data is quite different from financial data. To this end, we define a modified CS method using general CS as the base but with additional rules for high prediction performance. While this modified CS method maintains its explainability via a well-defined scorecard, it also shows comparable prediction performance as ML’s (AUROC 0.9509). The modified CS method is expected to affect residential DR in a positive way. Its high accuracy for predicting customer participation behavior means a large potential for improving efficiency. Its explainability means not only an easier interaction with customers but also less effort for educating call-center agents who need to deal with the customers.