Lasso estimation for GEFCom2014 probabilistic electric load forecasting

Florian Ziel and Bidong Liu

International Journal of Forecasting, 2016, vol. 32, issue 3, 1029-1037

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.

Keywords: Probabilistic forecasting; Threshold AR; Time-varying effects (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:1029-1037

DOI: 10.1016/j.ijforecast.2016.01.001

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