Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets

• Main Authors: Tryggvi Jónsson, Pierre Pinson, Henrik Aa. Nielsen, Henrik Madsen
• Format: Article
• Language: English
• Published: MDPI AG 2014-06-01
• Series: Energies
• Subjects: real-time electricity markets; classification; non-stationarity; moving average
• Online Access: http://www.mdpi.com/1996-1073/7/6/3710

The optimal design of offering strategies for wind power producers is commonly based on unconditional (and, hence, constant) expectation values for prices in real-time markets, directly defining their loss function in a stochastic optimization framework. This is why it may certainly be advantageous to account for the seasonal and dynamic behavior of such prices, hence translating to time-varying loss functions. With that objective in mind, forecasting approaches relying on simple models that accommodate the seasonal and dynamic nature of real-time prices are derived and analyzed. These are all based on the well-known Holt–Winters model with a daily seasonal cycle, either in its conventional form or conditioned upon exogenous variables, such as: (i) day-ahead price; (ii) system load; and (iii) wind power penetration. The superiority of the proposed approach over a number of common benchmarks is subsequently demonstrated through an empirical investigation for the Nord Pool, mimicking practical forecasting for a three-year period over 2008–2011.