forecasting

Yang Xiao

Ph.D. Student, Statistics
Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

Smart Meter Data-Driven Load Forecasting and Price Design in the Retail Market

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KAUST

The widespread popularity of smart meters enables an immense amount of fine-grained smart meter data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. Electricity retailer is one of the main participants in the retail market. This presentation will discuss how an electricity retailer can increase the competitiveness in the retail market by making full use of the fine-grained smart meter data. Since load forecasting is fundamental for various businesses of the retailer, the first part of this presentation will study how to enhance the performance of aggregated load forecasting using the fine-grained smart meter data. Designing customizing prices is an effective way to promote consumer interactions and increase customer stickiness for retailers. The second part of this presentation will introduce a novel data-driven approach for incentive-compatible customizing time-of-use (ToU) price design so that the benefits of both the retailer and consumers can be gained.