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Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships

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  • Katarzyna Maciejowska

    ()

  • Rafał Weron

    ()

Abstract

This paper investigates whether using hourly and/or zonal prices can improve the accuracy of short- and medium-term forecasts of average daily electricity prices. We consider a 6 years period (2008–2013) of hourly day-ahead prices from 19 zones of the Pennsylvania–New Jersey–Maryland (PJM) interconnection and the PJM Dominion Hub in Virginia, U.S. The predictive performance of four multivariate models calibrated to hourly and/or zonal day-ahead prices is evaluated and compared with that of a univariate model, which uses only average daily data for the Dominion Hub. The multivariate competitors include a restricted vector autoregressive model and three factor models with the common and idiosyncratic components estimated using principal components in a semiparametric setup. The results indicate that there are statistically significant forecast improvements from incorporating the additional information, essentially for all considered forecast horizons ranging from 1 day to 2 months, but only when the correlation structure of prices across locations and/or hours is modeled using factor models. Copyright The Author(s) 2015

Suggested Citation

  • Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
  • Handle: RePEc:spr:compst:v:30:y:2015:i:3:p:805-819
    DOI: 10.1007/s00180-014-0531-0
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    Cited by:

    1. repec:eee:rensus:v:81:y:2018:i:p1:p:1548-1568 is not listed on IDEAS
    2. García-Martos, Carolina & Bastos, Guadalupe & Alonso Fernández, Andrés Modesto, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-21, July.
    4. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Papers 1801.01093, arXiv.org.
    5. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    7. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    8. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    10. repec:eee:eneeco:v:70:y:2018:i:c:p:396-420 is not listed on IDEAS
    11. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.

    More about this item

    Keywords

    Wholesale electricity price; Forecasting; Vector autoregression; Factor model; Principal components; PJM market;

    JEL classification:

    • 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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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