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Structural combination of seasonal exponential smoothing forecasts applied to load forecasting

Juan F. Rendon-Sanchez and Lilian M. de Menezes

European Journal of Operational Research, 2019, vol. 275, issue 3, 916-924

Abstract: This article draws from research on ensembles in computational intelligence to propose structural combinations of forecasts, which are point forecast combinations that are based on information from the parameters of the individual models that generated the forecasts. Two types of structural combination are proposed which use seasonal exponential smoothing as base models, and are applied to forecast short-term electricity demand. Although forecasting performance may depend on how ensembles are generated, results show that the proposed combinations can outperform competitive benchmarks. The methods can be used to forecast other seasonal data and be extended to different types of forecasting models.

Keywords: Forecasting; Combination of forecasts; Electricity demand/load forecasting; Ensembles; Exponential smoothing methods (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:275:y:2019:i:3:p:916-924

DOI: 10.1016/j.ejor.2018.12.013

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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