Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy

Carlo Fezzi and Luca Mosetti

The Energy Journal, 2020, vol. 41, issue 4, 231-254

Abstract: Short-term electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the literature does not provide guidelines on how to select the optimal size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets (Nord Pool and IPEX) with a selection of eleven different forecasting models. Incidentally, our empirical application reveals that simple models, such as a simple linear regression (SLR) with only two parameters, can perform unexpectedly well if estimated on extremely short samples. Surprisingly, in the Nord Pool, such SLR is the best performing model in 13 out 24 trading periods.

Keywords: Electricity price forecasting; Day-ahead market; Parameter instability; Bandwidth selection; Statistical models; Artificial neural networks. (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.5547/01956574.41.4.cfez (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:enejou:v:41:y:2020:i:4:p:231-254

DOI: 10.5547/01956574.41.4.cfez

Access Statistics for this article

More articles in The Energy Journal
Bibliographic data for series maintained by SAGE Publications ().