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Load Forecasting and Electricity Consumption by Regression Model
https://orcid.org/http://orcid.org/0000-0002-7246-8740
https://orcid.org/http://orcid.org/0000-0002-4638-5263
https://orcid.org/http://orcid.org/0000-0003-4056-9771
https://orcid.org/http://orcid.org/0000-0002-0841-9055
https://orcid.org/http://orcid.org/0000-0002-0049-2979
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.
Load Forecasting and Electricity Consumption by Regression Model
https://orcid.org/http://orcid.org/0000-0002-7246-8740
https://orcid.org/http://orcid.org/0000-0002-4638-5263
https://orcid.org/http://orcid.org/0000-0003-4056-9771
https://orcid.org/http://orcid.org/0000-0002-0841-9055
https://orcid.org/http://orcid.org/0000-0002-0049-2979
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.
Load Forecasting and Electricity Consumption by Regression Model
https://orcid.org/http://orcid.org/0000-0002-7246-8740
https://orcid.org/http://orcid.org/0000-0002-4638-5263
https://orcid.org/http://orcid.org/0000-0003-4056-9771
https://orcid.org/http://orcid.org/0000-0002-0841-9055
https://orcid.org/http://orcid.org/0000-0002-0049-2979
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.
Load Forecasting and Electricity Consumption by Regression Model
Lect. Notes in Networks, Syst.
Arsenyeva, Olga (editor) / Romanova, Tatiana (editor) / Sukhonos, Maria (editor) / Tsegelnyk, Yevgen (editor) / Sukhonos, Maria (author) / Babaiev, Volodymyr (author) / Pliuhin, Vladyslav (author) / Teterev, Vitaliy (author) / Khudiakov, Illia (author)
International Conference on Smart Technologies in Urban Engineering ; 2022 ; Kharkiv, Ukraine
2022-11-29
13 pages
Article/Chapter (Book)
Electronic Resource
English
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