Version 1
: Received: 2 July 2016 / Approved: 2 July 2016 / Online: 2 July 2016 (03:48:36 CEST)
How to cite:
Harasheh, M. Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case. Preprints2016, 2016070001. https://doi.org/10.20944/preprints201607.0001.v1
Harasheh, M. Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case. Preprints 2016, 2016070001. https://doi.org/10.20944/preprints201607.0001.v1
Harasheh, M. Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case. Preprints2016, 2016070001. https://doi.org/10.20944/preprints201607.0001.v1
APA Style
Harasheh, M. (2016). Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case. Preprints. https://doi.org/10.20944/preprints201607.0001.v1
Chicago/Turabian Style
Harasheh, M. 2016 "Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case" Preprints. https://doi.org/10.20944/preprints201607.0001.v1
Abstract
Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.
Copyright:
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.
