Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model

Song Ding, Zui Tao, Huahan Zhang and Yao Li

Energy, 2022, vol. 239, issue PA

Abstract: Forecasting nuclear energy accurately is vital to ensure reliable electricity supply and alleviate environmental degradation problems. However, it is difficult to model the nuclear time series due to its complexity, nonlinearity, and uncertainty. To this end, this paper put forward an optimized structure-adaptive grey model by theoretically providing the generalized time response function and accurately modifying the background value based on Simpson's rule. Moreover, Monte-Carlo Simulation and probability density analysis (PDA) are employed to analyze the proposed model's merits, revealing its robustness and reliability. For validation and verification purposes, the optimized model is implemented to predict nuclear energy consumption in China and America, compared to seven benchmark models involving other prevalent grey models, conventional econometric technology, and artificial intelligence. Experimental results from two cases consistently demonstrate that the novel technique significantly outperforms the competitors from two different perspectives of PDA and level accuracy. Besides, further discussion over different forecasting horizons reveals that this new model can still deliver accurate forecasts with solid robustness and high reliability. Consequently, the proposed model is validated as a practical and promising model for forecasting nuclear energy consumption.

Keywords: Grey prediction; Structure-adaptative model; Grey system; Nuclear energy consumption (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021769

DOI: 10.1016/j.energy.2021.121928

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