UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China
The electricity distribution in a planned way has been a difficult issue that the power supply bureau wishes to solve, and it is the lifeblood of economic development. Forecasting annual electricity consumption is very crucial for the planning of the power supply bureau and for booming economic deve...
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2021
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oai:doaj.org-article:ec1bfa28e07646e8b5157b8839cb83122021-11-14T04:34:20ZUFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China2352-484710.1016/j.egyr.2021.09.105https://doaj.org/article/ec1bfa28e07646e8b5157b8839cb83122021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721009112https://doaj.org/toc/2352-4847The electricity distribution in a planned way has been a difficult issue that the power supply bureau wishes to solve, and it is the lifeblood of economic development. Forecasting annual electricity consumption is very crucial for the planning of the power supply bureau and for booming economic development. We propose a novel unbiased fractional nonlinear grey Bernoulli model [i.e, UFNGBM (1,1)] to forecast China’s annual electricity consumption based on the nonlinear grey Bernoulli model [i.e, NGBM (1,1)]. First, UFNGBM (1,1) approach is designed to derive calculation formula of the novel model, and validity of the model is proved by the matrix perturbation theory. Second, a novel optimization algorithm is introduced based on the whale algorithm to find the optimal parameters (i.e., orderand power) of the proposed model. Third, the accuracy, stability, and effectiveness of our method are verified through three real-world cases in China. Finally, we collect the electricity consumption data of three provinces in China and successfully apply the proposed algorithm to predict the electricity consumption from 2019 to 2024. The experimental results demonstrate that our proposed model is significantly superior to nine alternative models on the electricity consumption data of Jilin and Jiangsu. The performance of our novel method is close to the state-of-the-art deep learning method on the electricity consumption data of Shandong. It is noticed that our method [as an extended version of NGBM(1,1)] is significantly better than NGBM(1,1) on these three real-world datasets, which further shows the effectiveness of the our proposed algorithm. Meanwhile, the electricity consumption of these three provinces in the next six years (2019–2024) is forecasted, which has a very good guiding significance and provides a more reliable reference for the economic and power bureau.Bin PuFengtao NanNingbo ZhuYe YuanWanli XieElsevierarticleElectricity consumption forecastingElectricity economicsGrey modelNonlinear grey Bernoulli modelSix-year-planElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7405-7423 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Electricity consumption forecasting Electricity economics Grey model Nonlinear grey Bernoulli model Six-year-plan Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| spellingShingle |
Electricity consumption forecasting Electricity economics Grey model Nonlinear grey Bernoulli model Six-year-plan Electrical engineering. Electronics. Nuclear engineering TK1-9971 Bin Pu Fengtao Nan Ningbo Zhu Ye Yuan Wanli Xie UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| description |
The electricity distribution in a planned way has been a difficult issue that the power supply bureau wishes to solve, and it is the lifeblood of economic development. Forecasting annual electricity consumption is very crucial for the planning of the power supply bureau and for booming economic development. We propose a novel unbiased fractional nonlinear grey Bernoulli model [i.e, UFNGBM (1,1)] to forecast China’s annual electricity consumption based on the nonlinear grey Bernoulli model [i.e, NGBM (1,1)]. First, UFNGBM (1,1) approach is designed to derive calculation formula of the novel model, and validity of the model is proved by the matrix perturbation theory. Second, a novel optimization algorithm is introduced based on the whale algorithm to find the optimal parameters (i.e., orderand power) of the proposed model. Third, the accuracy, stability, and effectiveness of our method are verified through three real-world cases in China. Finally, we collect the electricity consumption data of three provinces in China and successfully apply the proposed algorithm to predict the electricity consumption from 2019 to 2024. The experimental results demonstrate that our proposed model is significantly superior to nine alternative models on the electricity consumption data of Jilin and Jiangsu. The performance of our novel method is close to the state-of-the-art deep learning method on the electricity consumption data of Shandong. It is noticed that our method [as an extended version of NGBM(1,1)] is significantly better than NGBM(1,1) on these three real-world datasets, which further shows the effectiveness of the our proposed algorithm. Meanwhile, the electricity consumption of these three provinces in the next six years (2019–2024) is forecasted, which has a very good guiding significance and provides a more reliable reference for the economic and power bureau. |
| format |
article |
| author |
Bin Pu Fengtao Nan Ningbo Zhu Ye Yuan Wanli Xie |
| author_facet |
Bin Pu Fengtao Nan Ningbo Zhu Ye Yuan Wanli Xie |
| author_sort |
Bin Pu |
| title |
UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| title_short |
UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| title_full |
UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| title_fullStr |
UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| title_full_unstemmed |
UFNGBM (1,1): A novel unbiased fractional grey Bernoulli model with Whale Optimization Algorithm and its application to electricity consumption forecasting in China |
| title_sort |
ufngbm (1,1): a novel unbiased fractional grey bernoulli model with whale optimization algorithm and its application to electricity consumption forecasting in china |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/ec1bfa28e07646e8b5157b8839cb8312 |
| work_keys_str_mv |
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