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A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation

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  • Li, Zekai
  • Hu, Xi
  • Guo, Huan
  • Xiong, Xin

Abstract

This paper proposes a novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model (WAWBO-FSGGM(1,1)) for accurate prediction on the hydropower generation. Firstly, to group the raw data with notable seasonality, this paper divides them into four groups to reorganize their order and establish a SGGM(1,1) model by utilizing the seasonal data grouping approach. Secondly, to eliminate the external interference, the WAWBO is applied for fitting the raw data and improving the predictive performance. Thirdly, to minimize the perturbation bound of the model, the fractional accumulated generating as a special data transformation is utilized by the fractional derivative parameters based on the series of the r-Fractional Accumulated Generating Operation (r-FAGO). Hence, a WAWBO-FSGGM(1,1) model is established to accurately predict the hydropower generation. Fourthly, to seek for a solution of the parameters that minimizes the predictive errors, the Whale Optimization Algorithm (WOA) is utilized for the WAWBO-FSGGM(1,1) model to further improve the predictive accuracy with fast-convergence. Finally, the empirical results demonstrate that the MAPE values of the WAWBO-FSGGM(1,1) model in five selected Chinese provinces and the U.S.A. are respectively 1.4182%, 1.7081%, 0.7971%, 2.3752%, 0.9062%, and 1.0056%, which are all the smallest among all the predicted results.

Suggested Citation

  • Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223009623
    DOI: 10.1016/j.energy.2023.127568
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    1. Valentina Sessa & Edi Assoumou & Mireille Bossy & Sofia G. Simões, 2021. "Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation," Clean Technol., MDPI, vol. 3(4), pages 1-23, December.
    2. Piyal Ekanayake & Lasini Wickramasinghe & J. M. Jeevani W. Jayasinghe & Upaka Rathnayake, 2021. "Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, July.
    3. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    4. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    5. Wang, Yuan & Shi, Rui & Zhang, Chen & He, Yanmin & Jiang, Hongyi & Kubota, Jumpei, 2022. "Structural changes and trends in China's renewable electricity production in the policy evolution process," Renewable Energy, Elsevier, vol. 182(C), pages 879-886.
    6. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    7. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    8. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    9. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    10. Chen, Hai-Bao & Pei, Ling-Ling & Zhao, Yu-Feng, 2021. "Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach," Energy, Elsevier, vol. 222(C).
    11. Feng, Suzhen & Zheng, Hao & Qiao, Yifan & Yang, Zetai & Wang, Jinwen & Liu, Shuangquan, 2022. "Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances," Applied Energy, Elsevier, vol. 311(C).
    12. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    13. Zolfaghari, Mehdi & Golabi, Mohammad Reza, 2021. "Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models," Renewable Energy, Elsevier, vol. 170(C), pages 1367-1381.
    14. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2019. "Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model," Renewable Energy, Elsevier, vol. 140(C), pages 70-87.
    15. Ye Li & Xue Bai & Giulio E. Cantarella, 2022. "Wind Power Installed Capacity Forecast Based on a Two-Parameter Variable-Weight Buffer Operator and Subsidy Strategy Research," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-14, March.
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