IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2095-d1075553.html
   My bibliography  Save this article

New Method of Modeling Daily Energy Consumption

Author

Listed:
  • Krzysztof Karpio

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Piotr Łukasiewicz

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Rafik Nafkha

    (Institute of Information Technology, Warsaw University of Life-SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland)

Abstract

At present, papers concerning energy consumption and forecasting are predominantly dedicated to various known techniques and their combinations. On the other hand, the research on load modeling and forecasting methodologies is quite limited. This paper presents a new approach concerning hourly energy consumption using a multivariate linear regression model. The proposed technique provides a way to accurately model day-to-day energy consumption using just a few selected variables. The number of data points required to describe a whole day’s consumption depends on the demanded precision, which is up to the user. This model is self-configurable and very fast. The applied model shows that four hours are sufficient to describe energy consumption during the remainder of a given day. We show that for about 84% of the data points, the relative error of the model is below 2.5%, and for all the data points the error does not exceed 7.5%. We obtained a mean relative uncertainty of 1.72% in the learning data set, and 1.69% and 1.82% in the two testing data sets, respectively. In addition, we conclude that the model can also detect days with unusual energy consumption.

Suggested Citation

  • Krzysztof Karpio & Piotr Łukasiewicz & Rafik Nafkha, 2023. "New Method of Modeling Daily Energy Consumption," Energies, MDPI, vol. 16(5), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2095-:d:1075553
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2095/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2095/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
    2. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
    3. 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.
    4. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    5. Sajal Ghosh, 2008. "Univariate time-series forecasting of monthly peak demand of electricity in northern India," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 1(4), pages 466-474.
    6. Tao Hong, 2014. "Energy Forecasting: Past, Present, and Future," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 32, pages 43-48, Winter.
    7. 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.
    8. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    9. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    10. Mojica, Jose L. & Petersen, Damon & Hansen, Brigham & Powell, Kody M. & Hedengren, John D., 2017. "Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments," Energy, Elsevier, vol. 118(C), pages 97-115.
    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. Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska, 2023. "Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland," Energies, MDPI, vol. 16(19), pages 1-17, September.

    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. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    2. Rafik Nafkha & Tomasz Ząbkowski & Krzysztof Gajowniczek, 2021. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers," Energies, MDPI, vol. 14(8), pages 1-17, April.
    3. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    4. Ahmed Ismail & Mustafa Baysal, 2023. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-19, July.
    5. 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).
    6. Xiaoyu Gao & Chengying Qi & Guixiang Xue & Jiancai Song & Yahui Zhang & Shi-ang Yu, 2020. "Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR," Energies, MDPI, vol. 13(22), pages 1-19, November.
    7. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    8. Xuguang Han & Jingming Su & Yan Hong & Pingshun Gong & Danping Zhu, 2022. "Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    9. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    10. 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.
    11. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    12. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    13. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    14. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    15. Daniel Kosiorowski & Dominik Mielczarek & Jerzy. P. Rydlewski, 2017. "Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview," Papers 1712.03797, arXiv.org.
    16. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    17. Francesco Neirotti & Michel Noussan & Stefano Riverso & Giorgio Manganini, 2019. "Analysis of Different Strategies for Lowering the Operation Temperature in Existing District Heating Networks," Energies, MDPI, vol. 12(2), pages 1-17, January.
    18. Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
    19. Alessandro Bosisio & Matteo Moncecchi & Andrea Morotti & Marco Merlo, 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience," Energies, MDPI, vol. 14(14), pages 1-23, July.
    20. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(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:5:p:2095-:d:1075553. 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.