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Electricity price forecasting

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  • Rafal Weron
  • Florian Ziel

Abstract

Electricity price forecasting (EPF) is a branch of energy forecasting on the interface between econometrics/statistics and engineering, which focuses on predicting the spot and forward prices in wholesale electricity markets. Its beginnings can be traced back to the early 1990s, when power sector deregulation led to the introduction of competitive markets in the UK and Scandinavia. The changes quickly spread throughout Europe and North America, and nowadays - in many countries worldwide - electricity is traded under market rules using spot and derivative contracts. Over the last 25 years, a variety of methods and ideas have been tried for EPF, with varying degrees of success. In this chapter we first briefly discuss the forecasting horizons and the types of forecasts, then review the forecasting tools and the evaluation techniques used in the EPF literature.

Suggested Citation

  • Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
  • Handle: RePEc:wuu:wpaper:hsc1808
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    References listed on IDEAS

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    Cited by:

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    2. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-13, October.
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    4. Javier Pórtoles & Camino González & Javier M. Moguerza, 2018. "Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach," Energies, MDPI, Open Access Journal, vol. 11(6), pages 1-21, June.
    5. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, Open Access Journal, vol. 11(6), pages 1-18, June.
    6. Bartosz Uniejewski & Rafał Weron, 2018. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models," Energies, MDPI, Open Access Journal, vol. 11(8), pages 1-26, August.
    7. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    8. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-20, September.
    9. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, Open Access Journal, vol. 11(6), pages 1-21, June.
    10. Christian Pape, 2017. "The impact of intraday markets on the market value of flexibility–Decomposing effects on profile and the imbalance costs," EWL Working Papers 1711, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Dec 2017.
    11. Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, Open Access Journal, vol. 10(11), pages 1-16, November.
    12. Zigui Jiang & Rongheng Lin & Fangchun Yang, 2018. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-19, August.
    13. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-18, August.
    14. Feihu Hu & Xuan Feng & Hui Cao, 2018. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing," Energies, MDPI, Open Access Journal, vol. 11(12), pages 1-18, November.
    15. Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 11(7), pages 1-27, July.
    16. Leopoldo Angrisani & Francesco Bonavolontà & Annalisa Liccardo & Rosario Schiano Lo Moriello & Francesco Serino, 2018. "Smart Power Meters in Augmented Reality Environment for Electricity Consumption Awareness," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-17, September.
    17. Christian Giovanelli & Seppo Sierla & Ryutaro Ichise & Valeriy Vyatkin, 2018. "Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices," Energies, MDPI, Open Access Journal, vol. 11(7), pages 1-22, July.

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    More about this item

    Keywords

    Electricity price forecasting; Probabilistic forecast; Ensemble forecast; Day-ahead market; Intraday market; Regression; Computational intelligence; Reduced-form model; Multi-agent simulation; Model evaluation; Predictive ability test;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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