Forecasting Models of Electricity Prices
Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla—La Mancha, Campus Universitario S/N, 13071 Ciudad Real, Spain
Energies 2017, 10(2), 160; https://doi.org/10.3390/en10020160
Submission received: 14 January 2017
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Revised: 20 January 2017
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Accepted: 20 January 2017
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Published: 29 January 2017
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
This book contains the successful invited submissions [1,2,3,4,5,6,7,8,9,10,11] to a Special Issue of Energies on the subject area of “Forecasting Models of Electricity Prices”.
The electric power industry has been in a transition from a centralized towards a deregulated production scheme since the early 1980s. Previous centralized schemes were based on electricity tariffs that were paid by the customers as a function of the aggregate cost of production. In the new unbundled scheme, price forecasting has become an important tool for electric companies and customers to decide on their production offers and demand bids and for regulators to characterize the degree of competition of the market.
Electricity prices have unique features that are not observed in other markets, such as weekly and daily seasonalities, on-peak vs. off-peak hours, price spikes, etc. The fact that electricity is not easily storable and the requirement of meeting the demand at all times makes the development of forecasting techniques a challenging issue.
This Special Issue includes the most important forecasting techniques applied to the forecasting of electricity prices, such as:
- Statistical time series models;
- Artificial Neural Networks;
- Wavelet transform models;
- Regime-switching Markov models;
- Fundamental market models;
- Equilibrium models;
- Ensemble and portfolio decision models.
The response to our call had the following statistics:
- Submissions (15);
- Publications (11);
- Rejections (4);
- Article types: Review Article (0); Research Article (11);
The authors’ geographical distribution (published papers) is:
- China (3)
- Spain (3)
- Portugal (2)
- Denmark (1)
- Poland (1)
- Taiwan (1)
Published submissions are related to a broad range of applications for load and price forecasting including classical Auto Regressive, heuristics, equilibrium methods, switching models, and combinations of them, among others.
We found the edition and the selection of papers for this book to be very inspiring and rewarding. We also thank the editorial staff and reviewers for their efforts and help during the process.
Conflicts of Interest
The authors declare no conflict of interest.
References
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Contreras, J. Forecasting Models of Electricity Prices. Energies 2017, 10, 160. https://doi.org/10.3390/en10020160
AMA Style
Contreras J. Forecasting Models of Electricity Prices. Energies. 2017; 10(2):160. https://doi.org/10.3390/en10020160
Chicago/Turabian StyleContreras, Javier. 2017. "Forecasting Models of Electricity Prices" Energies 10, no. 2: 160. https://doi.org/10.3390/en10020160
APA StyleContreras, J. (2017). Forecasting Models of Electricity Prices. Energies, 10(2), 160. https://doi.org/10.3390/en10020160
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