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GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models

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  • Dordonnat, V.
  • Pichavant, A.
  • Pierrot, A.

Abstract

This paper describes the forecasting methodology used by the team ADADA in the load forecasting track of GEFCom2014. The approach includes a semi-parametric regression model for point load forecasting, a multivariate time series simulation model for temperature forecasting, and a resampling strategy for building probabilistic forecasts from point load forecast residuals.

Suggested Citation

  • Dordonnat, V. & Pichavant, A. & Pierrot, A., 2016. "GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1005-1011.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1005-1011
    DOI: 10.1016/j.ijforecast.2015.11.010
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    References listed on IDEAS

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    1. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
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    Cited by:

    1. Kanda, Isao & Veguillas, J.M. Quintana, 2019. "Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1460-1468.
    2. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    3. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
    4. Moreno-Carbonell, Santiago & Sánchez-Úbeda, Eugenio F. & Muñoz, Antonio, 2020. "Rethinking weather station selection for electric load forecasting using genetic algorithms," International Journal of Forecasting, Elsevier, vol. 36(2), pages 695-712.
    5. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," LSE Research Online Documents on Economics 120774, London School of Economics and Political Science, LSE Library.
    6. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
    7. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    8. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    9. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.

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