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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;
Received: 14 January 2017 / Revised: 20 January 2017 / Accepted: 20 January 2017 / 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.


  1. Bello, A.; Reneses, J.; Muñoz, A. Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case. Energies 2016, 9, 193. [Google Scholar] [CrossRef]
  2. Cheng, C.; Chen, F.; Li, G.; Tu, Q. Market Equilibrium and Impact of Market Mechanism Parameters on the Electricity Price in Yunnan’s Electricity Market. Energies 2016, 9, 463. [Google Scholar] [CrossRef]
  3. Jiang, P.; Liu, F.; Song, Y. A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection. Energies 2016, 9, 618. [Google Scholar] [CrossRef]
  4. Uniejewski, B.; Nowotarski, J.; Weron, R. Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting. Energies 2016, 9, 621. [Google Scholar] [CrossRef]
  5. Osório, G.J.; Gonçalves, J.N.D.L.; Lujano-Rojas, J.M.; Catalão, J.P.S. Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term. Energies 2016, 9, 693. [Google Scholar] [CrossRef]
  6. Monteiro, C.; Ramirez-Rosado, I.J.; Fernandez-Jimenez, L.A.; Conde, P. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market. Energies 2016, 9, 721. [Google Scholar] [CrossRef]
  7. Cheng, C.; Luo, B.; Miao, S.; Wu, X. Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market. Energies 2016, 9, 804. [Google Scholar] [CrossRef]
  8. Bello, A.; Bunn, D.; Reneses, J.; Muñoz, A. Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices. Energies 2016, 9, 959. [Google Scholar] [CrossRef]
  9. Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies 2016, 9, 987. [Google Scholar] [CrossRef]
  10. Sánchez de la Nieta, A.A.; González, V.; Contreras, J. Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming. Energies 2016, 9, 1069. [Google Scholar] [CrossRef]
  11. Neupane, B.; Woon, W.L.; Aung, Z. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting. Energies 2017, 10, 77. [Google Scholar] [CrossRef]

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MDPI and ACS Style

Contreras, J. Forecasting Models of Electricity Prices. Energies 2017, 10, 160.

AMA Style

Contreras J. Forecasting Models of Electricity Prices. Energies. 2017; 10(2):160.

Chicago/Turabian Style

Contreras, Javier. 2017. "Forecasting Models of Electricity Prices" Energies 10, no. 2: 160.

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