Next Article in Journal
Instability Analysis of Supercritical CO2 during Transportation and Injection in Carbon Capture and Storage Systems
Next Article in Special Issue
The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company
Previous Article in Journal
Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees
Previous Article in Special Issue
Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process
Article Menu

Export Article

Open AccessArticle
Energies 2018, 11(8), 2039; https://doi.org/10.3390/en11082039

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

1
Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
2
Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Received: 29 June 2018 / Revised: 23 July 2018 / Accepted: 30 July 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
View Full-Text   |   Download PDF [1929 KB, uploaded 6 August 2018]   |  

Abstract

Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models. View Full-Text
Keywords: electricity spot price; day-ahead market; long-term seasonal component; LASSO; automated variable selection; variance stabilizing transformation electricity spot price; day-ahead market; long-term seasonal component; LASSO; automated variable selection; variance stabilizing transformation
Figures

Figure 1

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

Share & Cite This Article

MDPI and ACS Style

Uniejewski, B.; Weron, R. Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models. Energies 2018, 11, 2039.

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