[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/mar/magkse/202135.html
   My bibliography  Save this paper

Machine learning in energy forecasts with an application to high frequency electricity consumption data

Author

Listed:
  • Erik Heilmann

    (University of Kassel)

  • Janosch Henze

    (University of Kassel)

  • Heike Wetzel

    (University of Kassel)

Abstract
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing different model approaches and a standardized process of model selection. This paper provides a concise and comprehensible introduction to the topic by discussing the concept of machine learning in the context of energy economics and presenting an exemplary application to electricity load data. For this, we introduce and demonstrate the structured machine learning process containing the preparation, model selection and test of forecast models. This process is intended to serve as a general guideline for energy economists and practitioners who need to apply sophisticated forecast models.

Suggested Citation

  • Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:202135
    as

    Download full text from publisher

    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2021-papers/35-2021_heilmann.pdf
    File Function: First 202135
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    5. 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.
    6. Neto, João C. do L. & da Costa Junior, Carlos T. & Bitar, Sandro D.B. & Junior, Walter B., 2011. "Forecasting of energy and diesel consumption and the cost of energy production in isolated electrical systems in the Amazon using a fuzzification process in time series models," Energy Policy, Elsevier, vol. 39(9), pages 4947-4955, September.
    7. Keles, Dogan & Genoese, Massimo & Möst, Dominik & Ortlieb, Sebastian & Fichtner, Wolf, 2013. "A combined modeling approach for wind power feed-in and electricity spot prices," Energy Policy, Elsevier, vol. 59(C), pages 213-225.
    8. Ren, Ye & Suganthan, P.N. & Srikanth, N., 2015. "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 82-91.
    9. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    10. 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.
    11. Gebhard Kirchgässner & Jürgen Wolters & Uwe Hassler, 2013. "Introduction to Modern Time Series Analysis," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-642-33436-8, June.
    12. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    13. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    14. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    15. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    16. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    17. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    18. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    19. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    20. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    21. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    22. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    23. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, August.
    24. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    25. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    26. Barthelmie, R.J. & Murray, F. & Pryor, S.C., 2008. "The economic benefit of short-term forecasting for wind energy in the UK electricity market," Energy Policy, Elsevier, vol. 36(5), pages 1687-1696, May.
    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. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
    2. Erik Heilmann, 2021. "The impact of transparency policies on local flexibility markets in electrical distribution networks: A case study with artificial neural network forecasts," MAGKS Papers on Economics 202141, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

    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. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
    2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    3. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    4. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    5. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    6. Erik Heilmann, 2021. "The impact of transparency policies on local flexibility markets in electrical distribution networks: A case study with artificial neural network forecasts," MAGKS Papers on Economics 202141, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    8. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
    9. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    10. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    11. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    12. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    13. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    14. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    15. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    16. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    17. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    18. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    19. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    20. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.

    More about this item

    Keywords

    machine learning; electricity consumption forecast; artificial neural network; time series forecast;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:mar:magkse:202135. 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: Bernd Hayo (email available below). General contact details of provider: https://edirc.repec.org/data/vamarde.html .

    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.