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Energies 2014, 7(6), 3710-3732;

Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets

Department of Applied Mathematics,Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark
Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325,2800 Kgs. Lyngby, Denmark
Author to whom correspondence should be addressed.
Received: 31 March 2014 / Revised: 31 May 2014 / Accepted: 6 June 2014 / Published: 16 June 2014
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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. View Full-Text
Keywords: real-time electricity markets; classification; non-stationarity; moving average real-time electricity markets; classification; non-stationarity; moving average
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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