Forecasting energy demand using neural-network-based grey residual modification models

Yi-Chung Hu () and Peng Jiang
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Yi-Chung Hu: Fujian Agriculture and Forestry University
Peng Jiang: Chung Yuan Christian University

Journal of the Operational Research Society, 2017, vol. 68, issue 5, 556-565

Abstract: Abstract Grey forecasting models have taken an important role for forecasting energy demand, particularly the GM(1,1) model, because they are able to construct a forecasting model using a limited samples without statistical assumptions. To improve prediction accuracy of a GM(1,1) model, its predicted values are often adjusted by establishing a residual GM(1,1) model, which together form a grey residual modification model. Two main issues should be considered: the sign estimation for a predicted residual and the way the two models are constructed. Previous studies have concentrated on the former issue. However, since both models are usually established in the traditional manner, which is dependent on a specific parameter that is not easily determined, this paper focuses on the latter issue, incorporating the neural-network-based GM(1,1) model into a residual modification model to resolve the drawback. Prediction accuracies of the proposed neural-network-based prediction models were verified using real power and energy demand cases. Experimental results verify that the proposed prediction models perform well in comparison with original ones.

Keywords: energy demand; forecasting; grey theory; neural network; residual model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (11)

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DOI: 10.1057/s41274-016-0130-2

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