IJPAM: Volume 117, No. 1 (2017)

Title

ELECTRIC ENERGY PRICE FORECASTING:
DESCRIPTIVE ANALYSIS AND FEATURES SELECTION

Authors

Maria Teresa Grifa
Department of Engeneering and Computer Science
University of L'Aquila
Via Vetoio, 67100, Coppito (AQ), ITALY

Abstract

The present paper is focused on the analysis of electricity market, after its recent liberalization. In particular we provide a detailed analysis of the latter exploiting descriptive analysis and the feature selection approach for a multivariate time series dataset. Moreover we will apply a pool of regression models on the features selection methodology focusing our study on the 2014-Global Energy Forecasting Competition dataset.

History

Received: 2017-09-08
Revised: 2017-10-27
Published: December 1, 2017

AMS Classification, Key Words

AMS Subject Classification: 62H99, 62P20, 68U20
Key Words and Phrases: time series,electricity price forecasting, descriptive analysis, feature selection, machine learning

Download Section

Download paper from here.
You will need Adobe Acrobat reader. For more information and free download of the reader, see the Adobe Acrobat website.

Bibliography

1
René Aïd (auth.), Fred Espen Benth, Valery A. Kholodnyi, Peter Laurence (eds.)-Quantitative Energy Finance: Modeling, Pricing, and Hedging in Energy and Commodity Markets, Springer-Verlag New York (2014).

2
F. Cordoni, On variables selection in energy markets forecasting, HPA (2016).

3
L. Di Persio, A. Cecchin, F. Cordoni, Novel approaches to the energy load unbalance forecasting in the Italian electricity market, Journal of Mathematics in Industry, 7, No. 1, 1-5 (2017).

4
L. Di Persio, M. Frigo, Gibbs sampling approach to regime switching analysis of financial time series,Journal of Computational and Applied Mathematics, 300, pp. 43-55 (2016).

5
L. Di Persio, M. Frigo, Maximum likelihood approach to markov switching models,WSEAS Transactions on Business and Economics, 12, pp. 239-242 (2015).

6
L. Di Persio, I. Perin, An ambit stochastic approach to pricing electricity forward contracts: The case of the German Energy Market,Journal of Probability and Statistics,626020 (2015).

7
V. Kaminski,Energy markets, Incisive Media, Risk Books Series (2014).

8
G. SwindleValuation and Risk Management in Energy Markets CUP (2014).

9
A. Swishchuk,Modeling and Pricing Of Swaps For Financial and Energy Markets with Stochastic Volatilities,World Scientific Publishing Company (2013).

10
R. Weron,Modeling and forecasting electricity loads and prices: A statistical approach,John Wiley & Sons, Vol. 403 (2007).

11
R. Weron, Electricity price forecasting: A review of the state-of-the-art with a look into the future, International journal of forecasting , 30, No. 4 (2014), 1030-1081.

How to Cite?

DOI: 10.12732/ijpam.v117i1.15 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2017
Volume: 117
Issue: 1
Pages: 185 - 201


Google Scholar; DOI (International DOI Foundation); WorldCAT.

CC BY This work is licensed under the Creative Commons Attribution International License (CC BY).