IDEAS home Printed from https://ideas.repec.org/a/vrs/losutr/v11y2020i1p51-76n4.html
   My bibliography  Save this article

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Author

Listed:
  • Hammad Mahmoud A.

    (Arab Academy for Science, Technology and Maritime Transport,Alexandria, Egypt)

  • Jereb Borut
  • Rosi Bojan
  • Dragan Dejan

    (University of Maribor/Faculty of Logistics, Celje, Slovenia)

Abstract

Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

Suggested Citation

  • Hammad Mahmoud A. & Jereb Borut & Rosi Bojan & Dragan Dejan, 2020. "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 11(1), pages 51-76, February.
    • Handle: RePEc:vrs:losutr:v:11:y:2020:i:1:p:51-76:n:4
    DOI: 10.2478/jlst-2020-0004
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jlst-2020-0004
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jlst-2020-0004?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
    ---><---

    References listed on IDEAS

    as
    1. Yanbing Lin & Hongyuan Luo & Deyun Wang & Haixiang Guo & Kejun Zhu, 2017. "An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(8), pages 1-16, August.
    2. Gamze Nalcaci & Ayse Özmen & Gerhard Wilhelm Weber, 2019. "Long-term load forecasting: models based on MARS, ANN and LR methods," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 1033-1049, December.
    3. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
    4. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
    5. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    6. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    7. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    8. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    9. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    10. Abu-Shikhah, Nazih & Elkarmi, Fawwaz, 2011. "Medium-term electric load forecasting using singular value decomposition," Energy, Elsevier, vol. 36(7), pages 4259-4271.
    11. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    12. Rodolfo Gordillo-Orquera & Luis Miguel Lopez-Ramos & Sergio Muñoz-Romero & Paz Iglesias-Casarrubios & Diego Arcos-Avilés & Antonio G. Marques & José Luis Rojo-Álvarez, 2018. "Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings," Energies, MDPI, vol. 11(3), pages 1-18, February.
    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. Parichada Trairat & David Banjerdpongchai, 2022. "Multi-Objective Optimal Operation of Building Energy Management Systems with Thermal and Battery Energy Storage in the Presence of Load Uncertainty," Sustainability, MDPI, vol. 14(19), pages 1-26, October.
    2. Ngang Bassey Ngang & Bakare Kazeem & Ude Kinsley Okechukwu & Akaninyene Michael Joshua, 2021. "Optimized Electric Power Generation Expansion Planning Using Decomposition Technique," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 94-116.

    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. 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.
    2. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
    3. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    4. Chang, Yu Sang, 2014. "Comparative analysis of long-term road fatality targets for individual states in the US—An application of experience curve models," Transport Policy, Elsevier, vol. 36(C), pages 53-69.
    5. Ferrara, L. & Koopman, S J., 2010. "Common business and housing market cycles in the Euro area from a multivariate decomposition," Working papers 275, Banque de France.
    6. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    7. Bin Li & Mingzhen Lu & Yiyi Zhang & Jia Huang, 2019. "A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction," Energies, MDPI, vol. 12(20), pages 1-19, October.
    8. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    9. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    10. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    11. Caporale, Guglielmo Maria & Çatık, Abdurrahman Nazif & Huyuguzel Kısla, Gul Serife & Helmi, Mohamad Husam & Akdeniz, Coşkun, 2022. "Oil prices and sectoral stock returns in the BRICS-T countries: A time-varying approach," Resources Policy, Elsevier, vol. 79(C).
    12. Petros Pechlivanoglou & Jaap E. Wieringa & Tim de Jager & Maarten J. Postma, 2015. "The Effect of Financial and Educational Incentives on Rational Prescribing. A State‐Space Approach," Health Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 439-453, April.
    13. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    14. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.
    15. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    16. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
    17. Dilaver, Zafer & Hunt, Lester C., 2011. "Industrial electricity demand for Turkey: A structural time series analysis," Energy Economics, Elsevier, vol. 33(3), pages 426-436, May.
    18. Moonam, Hasan M. & Qin, Xiao & Zhang, Jun, 2019. "Utilizing data mining techniques to predict expected freeway travel time from experienced travel time," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 154-167.
    19. Cesar R. Van Der Laan & Marcos Tadeu C. Lélis & André Moreira Cunha, 2016. "External Capital Flows’ Management In The Great Recession: The Brazilian Experience (2007-2013)," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 035, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    20. Orair, Rodrigo Octávio & Silva, Wesley de Jesus, 2013. "Subnational Government Investment in Brazil: Estimation and Analysis by State Space Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 33(1), 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:vrs:losutr:v:11:y:2020:i:1:p:51-76:n:4. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.