IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4294-d1154701.html
   My bibliography  Save this article

T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting

Author

Listed:
  • Mengkun Liang

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Renjing Guo

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Hongyu Li

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Jiaqi Wu

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Xiangdong Sun

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract

Electricity is an essential resource that plays a vital role in modern society, and its demand has increased rapidly alongside industrialization. The accurate forecasting of a country’s electricity demand is crucial for economic development. A high-precision electricity forecasting framework can assist electricity system managers in predicting future demand and production more accurately, thereby effectively planning and scheduling electricity resources and improving the operational efficiency and reliability of the electricity system. To address this issue, this study proposed a hybrid forecasting framework called T-LGBKS, which incorporates TPE-LightGBM, k-nearest neighbor (KNN), and the Shapley additive explanation (SHAP) methods. The T-LGBKS framework was tested using Chinese provincial panel data from 2005 to 2021 and compared with seven other mainstream machine learning models. Our testing demonstrated that the proposed framework outperforms other models, with the highest accuracy ( R 2 = 0.9732 ). This study also analyzed the interpretability of this framework by introducing the SHAP method to reveal the relationship between municipal electricity consumption and socioeconomic characteristics (such as how changes in economic strength, traffic levels, and energy structure affect urban electricity demand). The findings of this study provide guidance for policymakers and assist decision makers in designing and implementing electricity management systems in China.

Suggested Citation

  • Mengkun Liang & Renjing Guo & Hongyu Li & Jiaqi Wu & Xiangdong Sun, 2023. "T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting," Energies, MDPI, vol. 16(11), pages 1-27, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4294-:d:1154701
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4294/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pollitt, M. & Yang, C-H. & Chen, H., 2017. "Reforming the Chinese Electricity Supply Sector: Lessons from International Experience," Cambridge Working Papers in Economics 1713, Faculty of Economics, University of Cambridge.
    2. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
    3. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    4. Meng, Ming & Li, Xinxin, 2022. "Evaluating the direct rebound effect of electricity consumption: An empirical analysis of the provincial level in China," Energy, Elsevier, vol. 239(PB).
    5. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    6. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    7. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    8. Li, Raymond & Leung, Guy C.K., 2012. "Coal consumption and economic growth in China," Energy Policy, Elsevier, vol. 40(C), pages 438-443.
    9. Ismail Aliyu Danmaraya & Sallahuddin Hassan, 2016. "Electricity Consumption and Manufacturing Sector Productivity in Nigeria: An Autoregressive Distributed Lag-bounds Testing Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 195-201.
    10. 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).
    11. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    12. Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
    13. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    14. Fang, Debin & Hao, Peng & Hao, Jian, 2019. "Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model," Energy, Elsevier, vol. 170(C), pages 730-743.
    15. Jia, Zhijie & Lin, Boqiang, 2021. "How to achieve the first step of the carbon-neutrality 2060 target in China: The coal substitution perspective," Energy, Elsevier, vol. 233(C).
    16. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    17. Zhu, Junpeng & Lin, Boqiang, 2022. "Resource dependence, market-oriented reform, and industrial transformation: Empirical evidence from Chinese cities," Resources Policy, Elsevier, vol. 78(C).
    18. Thomas Lindner & Jonas Puck & Alain Verbeke, 2022. "Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(7), pages 1307-1314, September.
    19. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    20. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    21. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
    22. A. Gürhan Kök & Kevin Shang & Şafak Yücel, 2018. "Impact of Electricity Pricing Policies on Renewable Energy Investments and Carbon Emissions," Management Science, INFORMS, vol. 64(1), pages 131-148, January.
    Full references (including those not matched with items on IDEAS)

    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. Kazemzadeh, Mohammad-Rasool & Amjadian, Ali & Amraee, Turaj, 2020. "A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting," Energy, Elsevier, vol. 204(C).
    2. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    3. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    4. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    5. Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.
    6. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    7. Tiwari, Aviral Kumar & Eapen, Leena Mary & Nair, Sthanu R, 2021. "Electricity consumption and economic growth at the state and sectoral level in India: Evidence using heterogeneous panel data methods," Energy Economics, Elsevier, vol. 94(C).
    8. Óscar Trull & J. Carlos García-Díaz & Alicia Troncoso, 2019. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    10. Shi, Changfeng & Zhao, Yi & Zhang, Chenjun & Pang, Qinghua & Chen, Qiyong & Li, Ang, 2022. "Research on the driving effect of production electricity consumption changes in the Yangtze River Economic Zone - Based on regional and industrial perspectives," Energy, Elsevier, vol. 238(PA).
    11. Zhang, Pan & Wang, Huan, 2022. "Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China," Energy, Elsevier, vol. 245(C).
    12. Wang, Yong & Yang, Zhongsen & Luo, Yongxian & Yang, Rui & Sun, Lang & Sapnken, Flavian Emmanuel & Narayanan, Govindasami, 2024. "A novel structural adaptive Caputo fractional order derivative multivariate grey model and its application in China's energy production and consumption prediction," Energy, Elsevier, vol. 312(C).
    13. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    14. Li, Shoujiang & Wang, Jianzhou & Zhang, Hui & Liang, Yong, 2024. "Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy," Energy, Elsevier, vol. 298(C).
    15. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    16. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    17. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    18. 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).
    19. Zhang, Chi & Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2017. "On electricity consumption and economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 353-368.
    20. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(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:gam:jeners:v:16:y:2023:i:11:p:4294-:d:1154701. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.