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Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Author

Listed:
  • Agnieszka Kowalska-Styczeń

    (Faculty of Organization and Management, Silesian University of Technology, 26-28 Roosevelta Street, 41-800 Zabrze, Poland)

  • Tomasz Owczarek

    (Faculty of Organization and Management, Silesian University of Technology, 26-28 Roosevelta Street, 41-800 Zabrze, Poland)

  • Janusz Siwy

    (Ebicom Sp. z o.o., 65 Sokolska Street, 40-087 Katowice, Poland)

  • Adam Sojda

    (Faculty of Organization and Management, Silesian University of Technology, 26-28 Roosevelta Street, 41-800 Zabrze, Poland)

  • Maciej Wolny

    (Faculty of Organization and Management, Silesian University of Technology, 26-28 Roosevelta Street, 41-800 Zabrze, Poland)

Abstract

In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.

Suggested Citation

  • Agnieszka Kowalska-Styczeń & Tomasz Owczarek & Janusz Siwy & Adam Sojda & Maciej Wolny, 2022. "Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland," Energies, MDPI, vol. 15(14), pages 1-23, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5129-:d:862891
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    References listed on IDEAS

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    1. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
    2. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
    3. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    4. Alberini, Anna & Prettico, Giuseppe & Shen, Chang & Torriti, Jacopo, 2019. "Hot weather and residential hourly electricity demand in Italy," Energy, Elsevier, vol. 177(C), pages 44-56.
    5. Amira Mouakher & Wissem Inoubli & Chahinez Ounoughi & Andrea Ko, 2022. "Expect : EXplainable Prediction Model for Energy ConsumpTion," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
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