Abstract
Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.
Highlights
IntroductionThe trend-seasonal pattern of electricity spot prices, which is known as the longterm seasonal component (LTSC), has always attracted the attention of energy analysts [1,2,3,4,5,6,7]
The trend-seasonal pattern of electricity spot prices, which is known as the longterm seasonal component (LTSC), has always attracted the attention of energy analysts [1,2,3,4,5,6,7].This is especially so when modeling the average daily prices in the medium- or the longterm
To address the question of whether the seasonal component approach is beneficial in the case of parameter-rich models, we have performed an extensive empirical study that involved a well-performing, least absolute shrinkage and selection operator (LASSO)-estimated autoregressive (LEAR) model with
Summary
The trend-seasonal pattern of electricity spot prices, which is known as the longterm seasonal component (LTSC), has always attracted the attention of energy analysts [1,2,3,4,5,6,7] This is especially so when modeling the average daily prices in the medium- or the longterm. The studies that have been published to date may be criticized for only utilizing parsimonious structures with a relatively small number of explanatory variables or features These are known to underperform when compared to parameter-rich models with hundreds of regressors that are estimated via the least absolute shrinkage and selection operator (LASSO) [12,13,14,15,16]. No comparisons have been made between different variants of the LTSC or with analogous models that do not utilize seasonal decomposition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
