IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v244y2022ipas0360544221028218.html
   My bibliography  Save this article

Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target

Author

Listed:
  • Li, Jinghua
  • Luo, Yichen
  • Wei, Shanyang

Abstract

Currently, system dynamics (SDs) is thought to be an effective method to forecast the evolution process of electricity consumption, which is of great significance for the long-term planning of the power system. However, for the traditional SDs forecast model, the interactions between system variables are not considered, resulting in that it may be invalid in the carbon-neutral situation where the inter-relationship between system variables is complex. Moreover, the electricity consumption of various industries is only simply superimposed in the traditional SDs model. This also leads to that the changes of future electricity consumption cannot be understood and grasped as a whole. Thus, a long-term load forecasting method involving carbon-neutral situation is proposed in the current work. First, a new system of influencing factors for electricity consumption containing carbon-neutral factors is constructed. Then, based on the information feedback mechanism, a new SDs forecast model is established, which can more accurately reflect the interaction of system variables. In addition, an electricity consumption identity is also developed to reveal the quantitative relationship between factors, electricity consumption, and carbon emissions at a holistic level. Finally, the evolution process of electricity consumption under various carbon-neutral scenarios is forecasted based on the new established forecast model. Correspondingly, the forecast results are expected to guide power system planning in the future carbon-neutral situation.

Suggested Citation

  • Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
  • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028218
    DOI: 10.1016/j.energy.2021.122572
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221028218
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.122572?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. Dehghan, Hamed & Amin-Naseri, Mohammad Reza & Nahavandi, Nasim, 2021. "A system dynamics model to analyze future electricity supply and demand in Iran under alternative pricing policies," Utilities Policy, Elsevier, vol. 69(C).
    2. Carvallo, Juan Pablo & Larsen, Peter H. & Sanstad, Alan H. & Goldman, Charles A., 2018. "Long term load forecasting accuracy in electric utility integrated resource planning," Energy Policy, Elsevier, vol. 119(C), pages 410-422.
    3. Meng, Ming & Wang, Lixue & Shang, Wei, 2018. "Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models," Energy, Elsevier, vol. 165(PA), pages 143-152.
    4. 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.
    5. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    6. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    7. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    8. Wang, Jianjun & Li, Li, 2016. "Sustainable energy development scenario forecasting and energy saving policy analysis of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 718-724.
    9. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    10. Huang, Liqiao & Liao, Qi & Qiu, Rui & Liang, Yongtu & Long, Yin, 2021. "Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19," Applied Energy, Elsevier, vol. 283(C).
    11. Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    12. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    13. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    14. 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).
    15. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    16. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    17. Mohammed, Nooriya A., 2018. "Modelling of unsuppressed electrical demand forecasting in Iraq for long term," Energy, Elsevier, vol. 162(C), pages 354-363.
    18. Bilgili, Faik & Koçak, Emrah & Bulut, Ümit & Kuloğlu, Ayhan, 2017. "The impact of urbanization on energy intensity: Panel data evidence considering cross-sectional dependence and heterogeneity," Energy, Elsevier, vol. 133(C), pages 242-256.
    19. Qu, Hui & Chen, Wei & Niu, Mengyi & Li, Xindan, 2016. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models," Energy Economics, Elsevier, vol. 54(C), pages 68-76.
    20. Song, Malin & Wang, Shuhong & Yu, Huayin & Yang, Li & Wu, Jie, 2011. "To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 5129-5134.
    21. Xie, Wanli & Wu, Wen-Ze & Liu, Chong & Zhao, Jingjie, 2020. "Forecasting annual electricity consumption in China by employing a conformable fractional grey model in opposite direction," Energy, Elsevier, vol. 202(C).
    22. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
    23. Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    24. Xu, Bin & Lin, Boqiang, 2018. "Assessing the development of China's new energy industry," Energy Economics, Elsevier, vol. 70(C), pages 116-131.
    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. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    2. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    3. Rao, Yanchun & Wang, Xiuli & Li, Hengkai, 2024. "Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light," Energy, Elsevier, vol. 294(C).
    4. 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).
    5. Zhou, Xiaoyang & Zhu, Qiuyun & Xu, Lei & Wang, Kai & Yin, Xiang & Mangla, Sachin Kumar, 2024. "The effect of carbon tariffs and the associated coping strategies: A global supply chain perspective," Omega, Elsevier, vol. 122(C).
    6. Wang, Longze & Zhang, Yan & Li, Zhehan & Huang, Qiyu & Xiao, Yuxin & Yi, Xinxing & Ma, Yiyi & Li, Meicheng, 2023. "P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy," Energy, Elsevier, vol. 285(C).
    7. Zhao, Jing & Zhang, Qin & Zhou, Dequn, 2023. "Can marketed on-grid price drive the realization of energy transition in China’s power industry under the background of carbon neutrality?," Energy, Elsevier, vol. 276(C).
    8. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    9. Li, Yanbin & Zhao, Ke & Zhang, Feng, 2023. "Identification of key influencing factors to Chinese coal power enterprises transition in the context of carbon neutrality: A modified fuzzy DEMATEL approach," Energy, Elsevier, vol. 263(PA).

    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. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    3. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    4. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    5. 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).
    6. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    7. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    8. Zhanyang Xu & Jian Xu & Chengxi Xu & Hong Zhao & Hongyan Shi & Zhe Wang, 2024. "Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province," Sustainability, MDPI, vol. 16(22), pages 1-23, November.
    9. Liu, Yitong & Xue, Dingyu & Yang, Yang, 2021. "Two types of conformable fractional grey interval models and their applications in regional electricity consumption prediction," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    10. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    11. Mollik, Sazib & Rashid, M.M. & Hasanuzzaman, M. & Karim, M.E. & Hosenuzzaman, M., 2016. "Prospects, progress, policies, and effects of rural electrification in Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 553-567.
    12. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    13. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    14. Carmen de la Cruz-Lovera & Alberto-Jesus Perea-Moreno & José Luis de la Cruz-Fernández & Francisco G. Montoya & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Analysis of Research Topics and Scientific Collaborations in Energy Saving Using Bibliometric Techniques and Community Detection," Energies, MDPI, vol. 12(10), pages 1-23, May.
    15. Zhang, Bo & Qu, Xue & Meng, Jing & Sun, Xudong, 2017. "Identifying primary energy requirements in structural path analysis: A case study of China 2012," Applied Energy, Elsevier, vol. 191(C), pages 425-435.
    16. Musa, S. Danlami & Zhonghua, Tang & Ibrahim, Abdullateef O. & Habib, Mukhtar, 2018. "China's energy status: A critical look at fossils and renewable options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2281-2290.
    17. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    18. Zhao, Zhenyu & Zhang, Yao & Yang, Yujia & Yuan, Shuguang, 2022. "Load forecasting via Grey Model-Least Squares Support Vector Machine model and spatial-temporal distribution of electric consumption intensity," Energy, Elsevier, vol. 255(C).
    19. Zhang, Linyun & Huang, Feiming & Lu, Lu & Ni, Xinwen, 2021. "Green financial development improving energy efficiency and economic growth: A study of CPEC area in COVID-19 era," IRTG 1792 Discussion Papers 2021-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.

    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:244:y:2022:i:pa:s0360544221028218. 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.