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中国管理科学

中国管理科学 ›› 2023, Vol. 31 ›› Issue (5): 279-286.doi: 10.16381/j.cnki.issn1003-207x.2020.1640

• 论文 • 上一篇    

基于可重复性分数阶灰色时间幂模型的中国水电消费预测研究

周伟杰1, 姜慧敏1, 成雨珂2, 党耀国3, 丁松4   

  1. 1.常州大学吴敬琏经济学院,江苏 常州213164； 2.南通理工学院创新创业学院,江苏 南通226006;3.南京航天航空大学经济与管理学院,江苏 南京211006;4.浙江财经大学经济学院,浙江 杭州310018
  • 收稿日期:2020-08-25 修回日期:2020-12-14 发布日期:2023-05-23
  • 通讯作者: 丁松(1992-),男(汉族),江苏淮安人，浙江财经大学经济学院，副教授,硕士生导师,研究方向:系统建模，Email:dingsong1129@163.com. E-mail:dingsong1129@163.com
  • 基金资助:
    国家自然科学基金资助项目(71701024, 71901191)；国家社会科学基金资助项目(20&ZD128,20CRK018, 21BJY007)；江苏省研究生科研与实践创新计划基金资助项目(KYCX22_2987)

Forecasting Chinese Hydropower Consumption Forecasting by Using the Repeatability Fractional Grey Time Power Model

ZHOU Wei-jie1, JIANG Hui-min1, CHENG Yu-ke2, DANG Yao-guo3, DING Song4   

  1. 1. Wu Jinglian School of Economics, Changzhou University, Changzhou 213164, China;2. School of Innovation and Entrepreneurship, Nantong University of Technology, Nantong 226006, China;3. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;4. School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China
  • Received:2020-08-25 Revised:2020-12-14 Published:2023-05-23
  • Contact: 丁松 E-mail:dingsong1129@163.com

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摘要： 准确预测水电消费，不仅有利于后续水能资源的开发与规划，而且对中国能源结构转型升级有着重要的意义。为此，本文引入分数阶灰色时间幂函数FGM(1,1,ta)模型，并利用文化算法对超参数r和a进行寻优，以预测水电消费序列。然而，正如大多数利用智能算法对带超参数的灰色模型优化一样，由于寻优的随机性，使得序列模拟和预测的可重复性较差。为了解决这一问题，利用蒙特卡洛模拟均值化方法，构建具有可重复性的FGM(1,1,ta)模型(RFGM(1,1,ta))，来提高建模结果的可重现性。与一些基准模型比较，验证了新模型对水电消费序列的建模精度。最后，研究了寻优次数对建模结果的鲁棒性。

关键词: RFGM(1,1,ta)；可重复；核密度；基准模型

Abstract: Accurately predicting hydropower consumption makes great sense to many countries, especially for the developing ones who have insufficient electricity supply. Thus, on one hand, accurate forecasts are conducive to the subsequent development and planning of hydropower resources. On the other hand, they can also provide solid references for the transformation and upgrading of the energy structure. To this end, a fractional grey time power model is introduced in this paper and its hyper-parameters r and a are determined by using the Cultural Algorithm, which presents strong adaptability to the characteristics of the hydropower consumption sequence. Subsequently, the property of this proposed model has been discussed, referring to the relationships with several prevailing grey prediction models. By doing this analysis, it is found that the proposed model can unify the GM(1,1),FGM(1,1),GM(1,1,t), and GM(1,1,t2) models. It means that this new model obtains high adaptability to various time series. However, just like most intelligent algorithms to optimize the hyper-parameters in the grey models, the simulation prediction results of the original sequence lack repeatability as the randomness in operation search. It means that the forecasted results differ when conducting the calculating operation for each time. In order to deal with such issues, the Monte Carlo simulation averaging method is initially used to construct a repeatable FGM(1,1,tα) model (namely RFGM(1,1,tα)). This new model can improve the reproducibility of prediction results. Compared with the benchmark models, the modeling accuracy of the proposed model is verified in terms of predicting the hydropower consumption sequence. Finally, the robustness of the number of optimal searches to the results is tested. Results show that the proposed model is the most robust and effective. Therefore, the proposed model can be considered as a promising tool for hydropower consumption forecasting because it has strong robustness and wide practicability.

Key words: RFGM(1,1,tα) model; repeatability; kernel density; benchmark models

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