IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v333y2023ics0306261922018591.html
   My bibliography  Save this article

Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks

Author

Listed:
  • Thangjam, Aditya
  • Jaipuria, Sanjita
  • Dadabada, Pradeep Kumar

Abstract

Long-term Load Forecasting (LTLF) plays a vital role in the planning of electric utilities. In the long run, utilities face various uncertainties caused by economic and environmental factors. These uncertainties have made LTLF more complex and inaccurate, thus, amplifying financial risks of utilities. A potent contributor to such losses in LTLF accuracy is economic shocks. This study proposes two probabilistic Time-Varying (TV) approaches to capture such shocks in LTLF and minimise accuracy loss, namely TV-XGB-X and TV-PR, and their combinations considering economic policy uncertainty. Both eXtreme Gradient Boosting (XGB) and Polynomial Regression (PR) are extended to include the long-run TV effects of economic shocks as eXogeneous predictors in this paper. These models and their combinations are compared with various non-TV approaches through experiments on the monthly electricity consumption of eight energy-intensive states in the United States. The results reveal that the proposed combined approaches outperform stand-alone models on all datasets. The findings of this study can help utilities in hedging financial risks under shocks.

Suggested Citation

  • Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018591
    DOI: 10.1016/j.apenergy.2022.120602
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922018591
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120602?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. Yixiao Li & Gloria Lin & Thomas Lau & Ruochen Zeng, 2019. "A Review of Changepoint Detection Models," Papers 1908.07136, arXiv.org.
    2. Li, Raymond & Woo, Chi-Keung & Cox, Kevin, 2021. "How price-responsive is residential retail electricity demand in the US?," Energy, Elsevier, vol. 232(C).
    3. Sumer, Kutluk Kagan & Goktas, Ozlem & Hepsag, Aycan, 2009. "The application of seasonal latent variable in forecasting electricity demand as an alternative method," Energy Policy, Elsevier, vol. 37(4), pages 1317-1322, April.
    4. Mukherjee, Sayanti & Vineeth, C.R. & Nateghi, Roshanak, 2019. "Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework," Applied Energy, Elsevier, vol. 235(C), pages 1561-1582.
    5. Lin, Boqiang & Liu, Chang, 2016. "Why is electricity consumption inconsistent with economic growth in China?," Energy Policy, Elsevier, vol. 88(C), pages 310-316.
    6. Lindberg, K.B. & Seljom, P. & Madsen, H. & Fischer, D. & Korpås, M., 2019. "Long-term electricity load forecasting: Current and future trends," Utilities Policy, Elsevier, vol. 58(C), pages 102-119.
    7. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    8. von Hirschhausen, Christian & Andres, Michael, 2000. "Long-term electricity demand in China -- From quantitative to qualitative growth?," Energy Policy, Elsevier, vol. 28(4), pages 231-241, April.
    9. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    10. Haddow, Abigail & Hare, Chris & Hooley, John & Shakir, Tamarah, 2013. "Macroeconomic uncertainty: what is it, how can we measure it and why does it matter?," Bank of England Quarterly Bulletin, Bank of England, vol. 53(2), pages 100-109.
    11. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    12. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    13. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
    14. Miss Stephanie Denis & Mr. Prakash Kannan, 2013. "The Impact of Uncertainty Shocks on the UK Economy," IMF Working Papers 2013/066, International Monetary Fund.
    15. Bidong Liu & Jiali Liu & Tao Hong, 2015. "Sister models for load forecast combination," HSC Research Reports HSC/15/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    16. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    17. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    18. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    19. Kalhori, M. Rostam Niakan & Emami, I. Taheri & Fallahi, F. & Tabarzadi, M., 2022. "A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting," Applied Energy, Elsevier, vol. 314(C).
    20. Xie, Jingrui & Hong, Tao, 2016. "GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1012-1016.
    21. 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.
    22. John Haslett & Adrian E. Raftery, 1989. "Space‐Time Modelling with Long‐Memory Dependence: Assessing Ireland's Wind Power Resource," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(1), pages 1-21, March.
    23. Ma, Rufei & Liu, Zhenhua & Zhai, Pengxiang, 2022. "Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence," Energy Economics, Elsevier, vol. 107(C).
    24. Michael Wallace & Angran Li & Allen Hyde, 2022. "The Great Recession Index: A Place-based Indicator for Countries, States, and Metropolitan Areas," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 401-426, February.
    25. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    26. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    27. Ashish Sen & S. Srivastava, 1975. "On tests for detecting change in mean when variance is unknown," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 479-486, December.
    28. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    29. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    30. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
    31. Adams, Samuel & Adedoyin, Festus & Olaniran, Eniola & Bekun, Festus Victor, 2020. "Energy consumption, economic policy uncertainty and carbon emissions; causality evidence from resource rich economies," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 179-190.
    32. Ang, B.W., 1987. "Structural changes and energy-demand forecasting in industry with applications to two newly industrialized countries," Energy, Elsevier, vol. 12(2), pages 101-111.
    33. Donatos, George S. & Mergos, George J., 1989. "Energy demand in Greece : The impact of the two energy crises," Energy Economics, Elsevier, vol. 11(2), pages 147-152, April.
    34. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    35. Behm, Christian & Nolting, Lars & Praktiknjo, Aaron, 2020. "How to model European electricity load profiles using artificial neural networks," Applied Energy, Elsevier, vol. 277(C).
    36. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    37. J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
    38. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    39. de Hoog, Julian & Abdulla, Khalid, 2019. "Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1451-1459.
    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. Yin, He & Yang, Mao-sen & Lan, Hai & Hong, Ying-Yi & Guo, Dong & Jin, Feng, 2024. "A hybrid graph attention network based method for interval prediction of shipboard solar irradiation," Energy, Elsevier, vol. 298(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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. 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.
    3. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    4. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    5. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    6. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    7. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
    8. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    9. Hansen, Stephen & McMahon, Michael & Tong, Matthew, 2019. "The long-run information effect of central bank communication," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 185-202.
    10. Wang, Jiqian & Ma, Feng & Bouri, Elie & Zhong, Juandan, 2022. "Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions," Energy Economics, Elsevier, vol. 108(C).
    11. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    12. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    13. Lu, Fei & Ma, Feng & Bouri, Elie & Liao, Yin, 2024. "Do commodity futures have a steering effect on the spot stock market in China? New evidence from volatility forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    14. 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).
    15. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    16. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    17. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    18. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    19. Gabriela Badareu & Marius Dalian Doran & Mihai Alexandru Firu & Ionuț Marius Croitoru & Nicoleta Mihaela Doran, 2024. "Exploring the Role of Robots and Artificial Intelligence in Advancing Renewable Energy Consumption," Energies, MDPI, vol. 17(17), pages 1-17, September.
    20. Massimo Ferrari Minesso & Frederik Kurcz & Maria Sole Pagliari, 2022. "Do words hurt more than actions? The impact of trade tensions on financial markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1138-1159, September.

    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:appene:v:333:y:2023:i:c:s0306261922018591. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.