Assuring explainability on demand response targeting via credit scoring

Kyungeun Lee, Hyesu Lee, Hyoseop Lee, Yoonjin Yoon, Eunjung Lee and Wonjong Rhee

Energy, 2018, vol. 161, issue C, 670-679

Abstract: 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 a 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.

Keywords: Residential DR; Explainability; Prediction performance; Credit scoring (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:161:y:2018:i:c:p:670-679

DOI: 10.1016/j.energy.2018.07.179

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