Next Article in Journal
The Energy and Environmental Performance of Ground-Mounted Photovoltaic Systems—A Timely Update
Next Article in Special Issue
Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term
Previous Article in Journal
Hysteresis Characteristic in the Hump Region of a Pump-Turbine Model
Previous Article in Special Issue
A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Energies 2016, 9(8), 621; doi:10.3390/en9080621

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Department of Operations Research, Wrocław University of Technology, 50-370 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Contreras
Received: 5 July 2016 / Revised: 27 July 2016 / Accepted: 29 July 2016 / Published: 5 August 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
View Full-Text   |   Download PDF [1015 KB, uploaded 5 August 2016]   |  

Abstract

In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall. View Full-Text
Keywords: electricity price forecasting; day-ahead market; autoregression; variable selection; stepwise regression; ridge regression; lasso; elastic net electricity price forecasting; day-ahead market; autoregression; variable selection; stepwise regression; ridge regression; lasso; elastic net
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Uniejewski, B.; Nowotarski, J.; Weron, R. Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting. Energies 2016, 9, 621.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top