An ensemble approach to GEFCom2017 probabilistic load forecasting

Andrew J. Landgraf

International Journal of Forecasting, 2019, vol. 35, issue 4, 1432-1438

Abstract: We present an ensembling approach to medium-term probabilistic load forecasting which ranked second out of 73 competitors in the defined data track of the GEFCom2017 qualifying match. In addition to being accurate, the ensemble method is highly scalable, due to the fact that it had to be applied to nine quantiles in ten zones and for six rounds. Candidate forecasts were generated using random settings for input data, covariates, and learning algorithms. The best candidate forecasts were averaged to create the final forecast, with the number of candidate forecasts being chosen based on their accuracy in similar validation periods.

Keywords: Electricity; Energy forecasting; Ensemble; Forecasting competitions; Quantile regression (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1432-1438

DOI: 10.1016/j.ijforecast.2019.02.003

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