Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets
AbstractThe 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.
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Jónsson, T.; Pinson, P.; Nielsen, H.A.; Madsen, H. Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets. Energies 2014, 7, 3710-3732.
Jónsson T, Pinson P, Nielsen HA, Madsen H. Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets. Energies. 2014; 7(6):3710-3732.Chicago/Turabian Style
Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik A.; Madsen, Henrik. 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets." Energies 7, no. 6: 3710-3732.