Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets

Energies

Tryggvi Jónsson¹  Pierre Pinson³  Henrik Aa. Nielsen² 

[1] Department of Applied Mathematics,Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark; E-Mails:;ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark; E-Mail:;Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325, 2800 Kgs. Lyngby, Denmark; E-Mail:

关键词: real-time electricity markets;    classification;    non-stationarity;    moving average;   

DOI  :  10.3390/en7063710

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
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