Abstract
Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly dependent on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use principal component analysis (PCA) to automate the procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows length. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance, and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperforms other forecast averaging schemes.
Highlights
In recent years, we have observed a dynamic transformation of energy markets, which encompasses changes in the generation structure and the creation of new trading opportunities.Since the establishment of competitive power exchanges, a growing share of electricity has been traded in day-ahead markets, where offers are placed before the noon of the day preceding the delivery.To give traders the opportunity to balance deviations from positions contracted in the day-ahead market, the spot markets have been complemented by intraday and balancing markets
The results indicate that in case of slightly misspecified models, the proposed principal component analysis (PCA)–based procedure significantly outperforms both best performing ex–post selected calibration window and weighted averaged windows (WAW) approach [19]
The forecasting performance is evaluated separately for each calibration window, and the results are shown in Figure 5—each dot represents the Mean Absolute Error (MAE) of forecasts obtained by calibrating the model to a sample of a certain length
Summary
To give traders the opportunity to balance deviations from positions contracted in the day-ahead market (due to the highly unpredictable generation from renewable sources), the spot markets have been complemented by intraday and balancing markets. Operation in such a complex environment becomes challenging for many market participants, as it requires taking various operational decisions, for example, generators need to decide, how much electricity to offer on a day-ahead market see [1] or how to structure the intraday trade [2]. An accurate prediction of electricity prices becomes an important issue for utility managers. The literature is rich in publications focusing on modelling and forecasting of spot prices
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