We consider an electric utility company that serves retail electricity customers over a discrete time horizon. In each period, the company observes the customers' consumption as well as high-dimensional features on customer characteristics and exogenous factors. A distinctive element of our work is that these features exhibit three types of heterogeneity---over time, customers, or both. Based on the consumption and feature observations, the company can dynamically adjust the retail electricity price at the customer level. The consumption depends on the features: there is an underlying structure of clusters in the feature space, and the relationship between consumption and features is different in each cluster. Initially, the company knows neither the underlying cluster structure nor the corresponding consumption models. We design a data-driven policy of joint spectral clustering and feature-based pricing and show that our policy achieves near-optimal performance, i.e., its average regret converges to zero at the fastest achievable rate. This work is the first to theoretically analyze joint clustering and feature-based pricing with different types of feature heterogeneity. Our case study based on real-life smart meter data from Texas illustrates that our policy increases company profits by more than 100% over three months relative to the company policy and is robust to various forms of model misspecification.
Bio: Yuexing Li is an Assistant Professor of Operations Management and Business Analytics at Johns Hopkins Carey Business School. His research centers around the design and analysis of data-driven algorithms for dynamic decision making under uncertainty. By leveraging and developing various statistical learning and machine learning methods, his works advance the theoretical understanding of cutting-edge data-driven analytics and create real impact across multiple industries. His works have won the INFORMS Data Mining Best Paper Competition and have been selected to present on the Spotlight track at the INFORMS RM&P Conference.