Modeling and Forecasting the Daily Load Curves for Electricity

• Activity Number: 344
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Jonathan Hosking*+ and Soumyadip Ghosh and Ramesh Natarajan
• Companies: IBM Research and IBM Research and IBM Research
• Keywords: time series ; state space model ; Smart Grid

Abstract

Electricity transmission and distribution systems are increasingly moving towards the use of a "Smart Grid", a densely instrumented network for electricity delivery that facilitates remote control, automation, and feedback to consumers. We describe a model of the daily load curve for residential electricity consumption that includes the effects of dynamic price incentives on the demand response, which is a topic of considerable interest in the Smart Grid. The model is based on a time series and regression framework in which the daily load curve is represented by a set of periodic spline basis functions, with the coefficients of the basis-functions evolving according to a linear Gaussian state-space model that incorporates mean shifts, day-of-week and holiday adjustments, temperature effects, as well as the dynamic price incentive effects. This modeling and forecasting methodology provides the ability to model intraday load substitution effects that are induced by the specified dynamic pricing schedules, and the ability to use fine-grained (5 to 15 minute interval) observational data without greatly increasing the computational cost of the estimation and forecasting procedures.