Dmitriy O. Afanasyev, Elena A. Fedorova, 2019

Applied Energy 236, 196-210

DOI: 10.1016/j.apenergy.2018.11.076

Article on ScienceDirect (open access till Jan 18, 2019)


Increasing the accuracy of short-term electricity price forecasting allows day-ahead power market participants to obtain a positive economic effect by bidding close to the equilibrium price. However the electricity price time- series is generally infested with extreme values due to high price volatility. This paper discusses the impact of outlier filtering on forecasting accuracy based on a recently introduced seasonal component autoregressive model. We consider such methods of outlier detection (with a priori defined cut-off parameter) as threshold, standard deviation, percentage, recursive, and moving filter on prices. It is shown that such data pre-processing often leads to the forecasting accuracy gain while the error decrease (relative to the approach without filtering) in a number of cases may reach 1.8–1.9% of the average weekly price (in absolute values). For an a priori defined cut-off parameter, the simple threshold and standard deviation filter on prices outperform other considered methods, and yield to the accuracy gain in 63% and 67% of cases, correspondingly. At the same time, in case of the out-of-sample filter parameter grid optimization all of the methods demonstrate comparable prediction power (equal to the marginal performance). But, practically speaking, such optimization is time-consuming and cannot be carried out on unavailable future data. As an competitive alternative, we propose a combined filter on prices based on a committee machine which uses the results of individual non-optimized algorithms and is not time-consuming, but gives accuracy comparable to the best one obtained for each of the studied electricity markets and leads to forecast gain in 63% of the considered cases.

Keywords: electricity price forecasting, outlier filtering, committee machine, model confidence set, long-term trend-seasonal component

Author comments

The authors are grateful to Gilenko E.V. (Ph.D., St. Petersburg State University, Russia) for his valuable and stimulating comments on the paper, as well as assist for translation. This paper also greatly benefited from the helpful comments and suggestions of three anonymous reviewers and Prof. J. Yan (Editor-in-chief). This work was supported by the Russian Foundation for Basic Research (RFBR) [Grant No. 16-06-00237 A]. The given in this study point of view of Dmitriy O. Afanasyev is not the official position of his employer JSC Greenatom and may not coincide with it.