IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v214y2021ics0360544220319381.html
   My bibliography  Save this article

Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering

Author

Listed:
  • Feng, Qianqian
  • Sun, Xiaolei
  • Hao, Jun
  • Li, Jianping

Abstract

Accurate installed capacity forecasting can provide effective decision-making support for planning development strategies and establishing national electricity policies. First, considering the data limitation in quantity and accuracy, this paper proposes a multi-factor installed capacity forecasting framework combining the fuzzy time series method and support vector regression. Compared with four benchmark models, the proposed model shows advantages in installed capacity prediction. Second, the predictability dynamics of national installed capacity are explored from the perspective of country clusters. It is revealed that highly predictable countries usually obtain high forecasting accuracy with all forecasting models and are less sensitive to forecasting models. Using the k-means clustering method, this paper divides 136 sample countries into four categories according to the predictability. Third, based on the mean impact value analysis, this paper differentiates and ranks the importance of input variables on installed capacity development. The two most important factors influencing installed capacity are installed capacity development in the previous period and population. Overall, these results are of practical value to the operating decisions of electric power enterprises and the electricity plans of governments.

Suggested Citation

  • Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319381
    DOI: 10.1016/j.energy.2020.118831
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220319381
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118831?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nepal, Rabindra & Paija, Nirash, 2019. "Energy security, electricity, population and economic growth: The case of a developing South Asian resource-rich economy," Energy Policy, Elsevier, vol. 132(C), pages 771-781.
    2. Kuriqi, Alban & Pinheiro, António N. & Sordo-Ward, Alvaro & Garrote, Luis, 2019. "Flow regime aspects in determining environmental flows and maximising energy production at run-of-river hydropower plants," Applied Energy, Elsevier, vol. 256(C).
    3. Shahbaz, Muhammad & Lean, Hooi Hooi, 2012. "The dynamics of electricity consumption and economic growth: A revisit study of their causality in Pakistan," Energy, Elsevier, vol. 39(1), pages 146-153.
    4. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    5. He, Yongxiu & Jiao, Jie & Chen, Qian & Ge, Sifan & Chang, Yan & Xu, Yang, 2017. "Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin," Energy, Elsevier, vol. 133(C), pages 9-22.
    6. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
    7. Shuyu Li & Xue Yang & Rongrong Li, 2018. "Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    8. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    9. Meng, Ming & Shang, Wei & Zhao, Xiaoli & Niu, Dongxiao & Li, Wei, 2015. "Decomposition and forecasting analysis of China's energy efficiency: An application of three-dimensional decomposition and small-sample hybrid models," Energy, Elsevier, vol. 89(C), pages 283-293.
    10. Staid, Andrea & Guikema, Seth D., 2013. "Statistical analysis of installed wind capacity in the United States," Energy Policy, Elsevier, vol. 60(C), pages 378-385.
    11. Rashad Aliyev & Sara Salehi & Rafig Aliyev, 2019. "Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting," Sustainability, MDPI, vol. 11(3), pages 1-13, February.
    12. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
    13. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    14. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    15. Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
    16. Luo, Jian & Hong, Tao & Fang, Shu-Cherng, 2018. "Benchmarking robustness of load forecasting models under data integrity attacks," International Journal of Forecasting, Elsevier, vol. 34(1), pages 89-104.
    17. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    18. Al-Bajjali, Saif Kayed & Shamayleh, Adel Yacoub, 2018. "Estimating the determinants of electricity consumption in Jordan," Energy, Elsevier, vol. 147(C), pages 1311-1320.
    19. Wang, Qiang & Chen, Xi, 2012. "China's electricity market-oriented reform: From an absolute to a relative monopoly," Energy Policy, Elsevier, vol. 51(C), pages 143-148.
    20. Juheng Zhang & Xiaoping Liu & Xiao-Bai Li, 2020. "Predictive Analytics with Strategically Missing Data," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1143-1156, October.
    21. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    22. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    23. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    24. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    25. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    26. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    27. Jingmin Wang & Jian Zhang & Jing Nie, 2016. "An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-14, August.
    28. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    29. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
    30. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    31. Dihrab, Salwan S. & Sopian, K., 2010. "Electricity generation of hybrid PV/wind systems in Iraq," Renewable Energy, Elsevier, vol. 35(6), pages 1303-1307.
    32. Lin, Boqiang & Omoju, Oluwasola E. & Okonkwo, Jennifer U., 2016. "Factors influencing renewable electricity consumption in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 687-696.
    33. Biresselioglu, Mehmet Efe & Kilinc, Dilara & Onater-Isberk, Esra & Yelkenci, Tezer, 2016. "Estimating the political, economic and environmental factors’ impact on the installed wind capacity development: A system GMM approach," Renewable Energy, Elsevier, vol. 96(PA), pages 636-644.
    34. Qunli Wu & Chenyang Peng, 2015. "Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(12), pages 1-15, December.
    35. Ersheng Pan & Dong Peng & Wangcheng Long & Yawei Xue & Lang Zhao & Jinchao Li, 2019. "Provincial Grid Investment Scale Forecasting Based on MLR and RBF Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, January.
    36. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    37. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jujie & Xu, Wenjie & Zhang, Yue & Dong, Jian, 2022. "A novel air quality prediction and early warning system based on combined model of optimal feature extraction and intelligent optimization," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Jujie Wang & Zhenzhen Zhuang & Liu Feng, 2022. "Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
    3. Li, Yi & Liu, Tianya & Xu, Jinpeng, 2023. "Analyzing the economic, social, and technological determinants of renewable and nonrenewable electricity production in China: Findings from time series models," Energy, Elsevier, vol. 282(C).
    4. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    5. Liu, Ling & Wang, Jujie, 2021. "Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network," Applied Energy, Elsevier, vol. 292(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    2. Xu, Guangyue & Yang, Hualiu & Schwarz, Peter, 2022. "A strengthened relationship between electricity and economic growth in China: An empirical study with a structural equation model," Energy, Elsevier, vol. 241(C).
    3. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    4. Lin, Boqiang & Raza, Muhammad Yousaf, 2021. "Analysis of electricity consumption in Pakistan using index decomposition and decoupling approach," Energy, Elsevier, vol. 214(C).
    5. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    6. Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
    7. Abbasi, Kashif Raza & Abbas, Jaffar & Tufail, Muhammad, 2021. "Revisiting electricity consumption, price, and real GDP: A modified sectoral level analysis from Pakistan," Energy Policy, Elsevier, vol. 149(C).
    8. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    9. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    10. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    11. Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
    12. Xiwen Cui & Shaojun E & Dongxiao Niu & Dongyu Wang & Mingyu Li, 2021. "An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target," Sustainability, MDPI, vol. 13(15), pages 1-21, August.
    13. Abbasi, Kashif Raza & Shahbaz, Muhammad & Jiao, Zhilun & Tufail, Muhammad, 2021. "How energy consumption, industrial growth, urbanization, and CO2 emissions affect economic growth in Pakistan? A novel dynamic ARDL simulations approach," Energy, Elsevier, vol. 221(C).
    14. Ismail Shah & Hasnain Iftikhar & Sajid Ali, 2020. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique," Forecasting, MDPI, vol. 2(2), pages 1-17, May.
    15. Li, Yi & Liu, Tianya & Xu, Jinpeng, 2023. "Analyzing the economic, social, and technological determinants of renewable and nonrenewable electricity production in China: Findings from time series models," Energy, Elsevier, vol. 282(C).
    16. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    17. Meng, Ming & Mander, Sarah & Zhao, Xiaoli & Niu, Dongxiao, 2016. "Have market-oriented reforms improved the electricity generation efficiency of China's thermal power industry? An empirical analysis," Energy, Elsevier, vol. 114(C), pages 734-741.
    18. Lin, Jiang & Xu Liu, & Gang He,, 2020. "Regional electricity demand and economic transition in China," Utilities Policy, Elsevier, vol. 64(C).
    19. Magazzino, Cosimo & Drago, Carlo & Schneider, Nicolas, 2023. "Evidence of supply security and sustainability challenges in Nigeria’s power sector," Utilities Policy, Elsevier, vol. 82(C).
    20. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.