A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting

Jing Huang and Matthew Perry

International Journal of Forecasting, 2016, vol. 32, issue 3, 1081-1086

Abstract: The aim of this work is to produce probabilistic forecasts of solar power for the Global Energy Forecasting Competition 2014 (GEFCom2014). The task involves predicting the outputs from three solar farms at an hourly resolution using data from the ECMWF numerical weather prediction model.

Keywords: Solar power; Probabilistic forecasting; Gradient boosting; k-nearest neighbors regression; GEFCom2014 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:1081-1086

DOI: 10.1016/j.ijforecast.2015.11.002

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