Not everybody responds to a signal — How to target more responsive customers with explainability
15 Oct 2018
- A quantitative analysis of data-driven targeting in residential DR.
- Explainability of data-driven actions and its relation to fairness.
- Details of implementing credit scoring, which has good explainability, for DR.
- A case study of incentive DR, where the DR was operated through a smartphone app.
- Credit scoring can achieve comparable performance as machine learning methods.
As data-driven innovation becomes a main trend in the energy sector, explainability of data-driven actions is becoming a major fairness issue for the residential applications, and it is expected to become a requirement for regulatory compliance. Explainability, however, often demands a sacrifice in prediction performance and affects the effectiveness of data-driven actions. In this study, we consider data-driven customer targeting in an incentive-based residential demand response program, and investigate the explainability-performance tradeoff when using simple-rule based machine learning, and credit scoring methods. Credit scoring, that has been a popular solution in the finance discipline for over 60 years, is a scorecard based modeling method that can surely provide explainability. We first provide the detailed steps of applying credit scoring to the demand response problem. Then, we use a dataset of 14,525 households obtained from a real demand response program and analyze two prediction problems — participation prediction and behavior change prediction. The results show that credit scoring can achieve comparable performance as the best-performing machine learning methods while providing full explainability. Our results suggest that credit scoring can be a promising explainability option for broader energy sector problems.