{"id":6697,"date":"2026-02-22T22:05:46","date_gmt":"2026-02-22T22:05:46","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=6697"},"modified":"2026-03-06T09:50:34","modified_gmt":"2026-03-06T09:50:34","slug":"28-1-3-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-1-3-en\/","title":{"rendered":"28.1.3 En"},"content":{"rendered":"\n

Comparative statistical modeling of dynamic series for forecasting daily electricity consumption in Python, R, C#, C++, Go, and Java<\/strong><\/h1>\n\n\n\n

A.E. Dzgoev, Xiang Hua, A.D. Lagunova, Ya.A. Kopylova, D.V. Morozov, Ya.V. Mazhey, A.V. Brailovsky, D.A. Yudin, R. Allabergenov<\/strong><\/p>\n\n\n\n

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Abstract<\/strong>:<\/strong> <\/em>Forecasting electricity consumption is an important tool for energy companies to ensure the stability and economic efficiency of the national energy system. For large industrial enterprises, accurate
forecasting allows them to optimize production costs and avoid financial losses due to imbalances and high electricity tariffs.
Aim<\/strong>. The study is to construct a detailed step-by-step algorithm for developing an adequate mathematical model for hourly forecasting of electricity consumption at an enterprise, using the method of statistical analysis of dynamic series in various programming languages.
Materials and methods<\/strong>. The modeling and forecasting algorithm is based on the classical ordinary least squares (OLS) method for small data samples, as well as the moving matrix method. The mathematical apparatus of the data processing method was implemented using the Mathcad Express engineering software. The implementation of the data processing method using modern programming languages is demonstrated: Python, R, C#, C++, Go, and Java.
Results<\/strong>. The authors implemente an algorithm for calculating daily electricity consumption forecasts using the classical sliding matrix method in Python, R, C#, C++, Go, and Java for subsequent code comparison. The authors present the results of a comparison of the forecasting algorithm implementations based on the following criteria: number of lines of code and execution time, use of external resources, parallelism support, and code size (in characters). Specific examples demonstrate that the choice of programming language depends on the problem being solved by researchers and developers. The adequacy of the developed regression model is statistically proven, and the equation quality is verified. Confidence intervals for the error corridor of the forecast model have been calculated.
Conclusions<\/strong>. The study demonstrate that the task of system data analysis and energy consumption forecasting is effectively solved using the Python programming language. The code for implementing the classical sliding matrix method is available in an open repository on GitHub at the following link: https:\/\/github.com\/CollaborativeProgrammingTeam\/Method-of-Classical-sliding-matrix.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> classical sliding matrix method, mathematical statistics, adequate regression model, quality assessment, forecasting of power consumption, comparison of programming languages, regression problem, comparative software implementation<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Dzgoev A.E., Xiang Hua, Lagunova A.D., Kopylova Ya.A., Morozov D.V., Mazhey Ya.V., Brailovsky A.V., Yudin D.A., Allabergenov R. Comparative statistical modeling of dynamic series in forecasting daily electricity consumption in Python, R, C#, C++, Go, Java \/\/ News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 1. Pp. 39\u201356. DOI: 10.35330\/1991-6639-2026-28-1-39-56<\/p>\n\n\n\n

\u00a9   Dzgoev A.E., Xiang Hua, Lagunova A.D., Kopylova Ya.A., Morozov D.V., Mazhey Ya.V., Brailovsky A.V., Yudin D.A., Allabergenov R., 2026<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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    Information about the author<\/strong>s<\/summary>\n
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    Alan E. Dzgoev<\/strong>, Candidate of Technical Sciences, Associate Professor, Associate Professor of the Digital Transformation Department, Institute of Information Technology, MIREA \u2013 Russian Technological
    University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    dzgoev@mirea.ru, ORCID: https:\/\/orcid.org\/0000-0002-1314-6151, SPIN-code: 8092-8784<\/p>\n\n\n\n

    Xiang Hua<\/strong>, Candidate of Technical Sciences, Senior Researcher, Institute of Mechanical Engineering, Beijing Institute of Technology;
    5, Zhongguancun street, Weigongcun, Bei Jing Shi, Haidian District, Beijing, 100811, China;
    huaxiang@bit.edu.cn, ORCID: https:\/\/orcid.org\/0000-0003-4429-1893
    Anna D. Lagunova<\/strong>, Candidate of Economic Sciences, Associate Professor, Head of the Digital Transformation Department, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    lagunova@mirea.ru, ORCID: https:\/\/orcid.org\/0000-0003-3572-8192, SPIN-code: 4067-3038
    Yana A. Kopylova<\/strong>, Assistant, Digital Transformation Department, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    kopylova_y@mirea.ru, ORCID: https:\/\/orcid.org\/0009-0001-0060-6753, SPIN-code: 4909-1501
    Daniil V. Morozov<\/strong>, Assistant, Digital Transformation Department, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    morozov_dav@mirea.ru, ORCID: https:\/\/orcid.org\/0009-0005-5187-4124, SPIN-code: 6133-0974
    Yaroslav V. Mazhey<\/strong>, Assistant Professor, Department of Digital Transformation, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    mazhej@mirea.ru, ORCID: https:\/\/orcid.org\/0009-0003-9115-2295, SPIN-code: 5038-3572
    Andrey V. Brailovsky<\/strong>, Assistant Professor, Department of Digital Transformation, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    brajlovskij@mirea.ru, ORCID: https:\/\/orcid.org\/0009-0006-1794-7825, SPIN-code: 5900-1835
    Dmitry \u0410. Yudin<\/strong>, Student majoring in Software Engineering, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    yudin.d.a@edu.mirea.ru, ORCID: https:\/\/orcid.org\/0009-0005-9587-2016
    Ruslan Allabergenov<\/strong>, Student majoring in Software Engineering, Institute of Information Technology, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    ruslan_tm2003@mail.ru, ORCID: https:\/\/orcid.org\/0009-0002-5525-6524<\/p>\n\n\n\n

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    Funding<\/strong><\/summary>\n

    The study was performed without external funding.<\/p>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Comparative statistical modeling of dynamic series for forecasting daily electricity consumption in Python, R, C#, C++, Go, and Java A.E. Dzgoev, Xiang Hua, A.D. Lagunova, Ya.A. Kopylova, D.V. Morozov, Ya.V. Mazhey, A.V. Brailovsky, D.A. Yudin, R. Allabergenov Upload the full text Abstract: Forecasting electricity consumption is an important tool for energy companies to ensure the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-6697","page","type-page","status-publish","hentry"],"yoast_head":"\n28.1.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-1-3-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"28.1.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Comparative statistical modeling of dynamic series for forecasting daily electricity consumption in Python, R, C#, C++, Go, and Java A.E. Dzgoev, Xiang Hua, A.D. Lagunova, Ya.A. Kopylova, D.V. Morozov, Ya.V. Mazhey, A.V. Brailovsky, D.A. Yudin, R. 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