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Load Forecasting and Electricity Consumption by Regression Model

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Smart Technologies in Urban Engineering (STUE 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 536))

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Abstract

A non-standard method of forecasting load and power consumption using a regression model is considered. The training sample is taken as a basis, which is a set of values of the maximum annual load of the power system for the past ten years. The work is devoted to the issues of optimal design of the regression equation in the form of a time polynomial (polynomial) in order to ensure a high value of forecasting for the next five years and to estimate the confidence intervals of the forecast. A feature of this method of forecasting is the ability to find the values of the time series based on its historical values in energy. Which is the basis for planning, managing and optimizing energy production and control. Selection of informative indicators and determination of the type of model are considered separately. The statistical capacity of the model is being tested. However, the absence of autoregressive connections is checked. Calculation of interval estimates of the indicator and forecasting errors. Forecasting by regression model. In contrast to previous research, the authors have shown how to achieve a high rate of forecasting using the Python programming language and NumPy libraries. The results of the work will be useful for machine learning technologies, data science, statistics, energy companies and power systems and others.

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Authors and Affiliations

  1. O. M. Beketov National University of Urban Economy in Kharkiv, 17 Marshala Bazhanova Street, Kharkiv, 61002, Ukraine

    Maria Sukhonos, Volodymyr Babaiev, Vladyslav Pliuhin, Vitaliy Teterev & Illia Khudiakov

Authors
  1. Maria Sukhonos
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  2. Volodymyr Babaiev
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  3. Vladyslav Pliuhin
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  4. Vitaliy Teterev
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  5. Illia Khudiakov
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Corresponding author

Correspondence to Vladyslav Pliuhin .

Editor information

Editors and Affiliations

  1. O.M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine

    Olga Arsenyeva

  2. A. Pidhornyi Institute of Mechanical Engineering Problems, National Academy of Sciences, Kharkiv, Ukraine

    Tatiana Romanova

  3. O.M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine

    Maria Sukhonos

  4. O.M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine

    Yevgen Tsegelnyk

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Sukhonos, M., Babaiev, V., Pliuhin, V., Teterev, V., Khudiakov, I. (2023). Load Forecasting and Electricity Consumption by Regression Model. In: Arsenyeva, O., Romanova, T., Sukhonos, M., Tsegelnyk, Y. (eds) Smart Technologies in Urban Engineering. STUE 2022. Lecture Notes in Networks and Systems, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-031-20141-7_28

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