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Lasso estimation for GEFCom2014 probabilistic electric load forecasting

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  • Ziel, Florian
  • Liu, Bidong

Abstract

We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach incorporates the temperature effects directly, and reflects daily, weekly, and annual seasonal patterns and public holiday effects. We provide two empirical studies, one based on the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014 (GEFCom2014-L), and the other based on another recent probabilistic load forecasting competition that follows a setup similar to that of GEFCom2014-L. In both empirical case studies, the proposed methodology outperforms two multiple linear regression based benchmarks from among the top eight entries to GEFCom2014-L.

Suggested Citation

  • Ziel, Florian & Liu, Bidong, 2016. "Lasso estimation for GEFCom2014 probabilistic electric load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1029-1037.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1029-1037
    DOI: 10.1016/j.ijforecast.2016.01.001
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    1. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    2. Florian Ziel, 2015. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes," Papers 1502.06557, arXiv.org, revised Dec 2015.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    5. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    6. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    7. 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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    6. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    7. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    8. Karpinska, Lilia & Śmiech, Sławomir, 2020. "Conceptualising housing costs: The hidden face of energy poverty in Poland," Energy Policy, Elsevier, vol. 147(C).
    9. 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.
    10. Papież, Monika & Śmiech, Sławomir & Frodyma, Katarzyna, 2018. "Determinants of renewable energy development in the EU countries. A 20-year perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 918-934.
    11. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
    12. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.
    13. Roach, Cameron, 2019. "Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1439-1450.
    14. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    15. Rostami-Tabar, Bahman & Ziel, Florian, 2022. "Anticipating special events in Emergency Department forecasting," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1197-1213.
    16. 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.
    17. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    18. Jens Kley-Holsteg & Florian Ziel, 2020. "Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso," Papers 2005.04522, arXiv.org.
    19. Ziel, Florian, 2019. "Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1400-1408.
    20. Florian Ziel, 2020. "Load Nowcasting: Predicting Actuals with Limited Data," Energies, MDPI, vol. 13(6), pages 1-15, March.
    21. 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.
    22. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
    23. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    24. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.

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