{"id":15405,"date":"2026-06-24T13:06:09","date_gmt":"2026-06-24T12:06:09","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=15405"},"modified":"2026-06-30T09:59:38","modified_gmt":"2026-06-30T08:59:38","slug":"28-3-4-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-3-4-en\/","title":{"rendered":"28.3.4 En"},"content":{"rendered":"\n

Hourly load forecasting via PatchTST and Temporal Fusion Transformers: comparative system analysis with CatBoost and N-HiTS network models<\/strong><\/h2>\n\n\n\n

A.E. Dzgoev, E.V. Klimkin, V.V. Chernyauskas, A.V. Brailovsky, R.N. Rezenkov<\/strong><\/p>\n\n\n\n

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Abstract<\/strong><\/strong>. <\/em>Under modern conditions of dynamic demand behavior and external factors, traditional statistical autoregressive modeling methods (ARIMA, SARIMAX) are increasingly outperformed by machine learning algorithms (Gradient Boosting) and modern deep learning architectures (Transformers).
Aim<\/strong>. This study aims to address the problem of selecting the optimal model for next-day hourly electricity consumption forecasting under limited information constraints, utilizing advanced machine and deep learning methods such as CatBoost, Temporal Fusion Transformer (TFT), PatchTST, and N-HiTS.
Materials and methods<\/strong>. The source code was developed in Python and is publicly available as an open-source repository on GitHub at https:\/\/github.com\/KEV0143\/Comparative-analysis-of-hourly-loadforecasting-using-PatchTST-TFT-NHiTS-and-CatBoost.
Results<\/strong>. New highly accurate and robust mathematical models for electricity consumption forecasting were successfully developed for each of the evaluated methods. A key mathematical outcome of this study demonstrates that effective electricity consumption forecasting for corporate energy conservation can be formulated as a scientifically substantiated selection of the single optimal model. This approach is crucial for both the theoretical analysis of advanced machine learning algorithms and their practical deployment,
driven by the rapidly growing volume of data. Addressing this issue is highly significant for energy consumption optimization across individual enterprises and entire regions.
Conclusion<\/strong>. The theoretical analysis and computational experiments utilizing empirical data from a Russian energy company provided valuable practical insights into the applicability of the CatBoost model for industrial energy consumption optimization. All conclusions presented in this paper are rigorously supported by statistical test results.<\/p>\n\n\n\n

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Keywords<\/strong><\/em><\/strong>:<\/em> systems analysis of models, mathematical statistics, short-term load forecasting, electricity consumption, comparative analysis, machine learning, deep learning, time series analysis, CatBoost, Temporal Fusion Transformer (TFT), PatchTST, N\u2011HiTS<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Dzgoev A.E., Klimkin E.V., Chernyauskas V.V., Brailovsky A.V., Rezenkov R.N. Hourly load forecasting via PatchTST and Temporal Fusion Transformers: comparative system analysis with CatBoost and N-HiTS network models. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 3. Pp. 49\u201370. DOI: 10.35330\/1991-6639-2026-28-3-49-70<\/p>\n\n\n\n

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\u00a9   Dzgoev A.E., Klimkin E.V., Chernyauskas V.V., Brailovsky A.V., Rezenkov R.N., 2026<\/p>\n\n\n\n

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Content is available under license Creative Commons Attribution 4.0 License<\/a><\/p>\n<\/div>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  21. Nie Y., Nguyen N.H., Sinthong P., Kalagnanam J. A time series is worth 64 words: long-term forecasting with transformers (PatchTST). Proceedings of the International Conference on Learning Representations. International Conference on Learning Representations ICLR 2023. https:\/\/arxiv.org\/pdf\/2211.14730.pdf<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n
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    Alan E. Dzgoev<\/strong>, Candidate of Technical Sciences, Associate Professor, Associate Professor at the Department of Digital Transformation, Institute of Information Technologies, 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
    Egor V. Klimkin<\/strong>, Undergraduate Student, Department of Applied Mathematics, Institute of Information Technologies, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia.;
    KlimkinEVK@yandex.ru, ORCID: https:\/\/orcid.org\/0009-0001-3876-3041
    Vladislav V. Chernyauskas<\/strong>, Senior Lecturer, Department of Digital Transformation, Institute of Information Technologies, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    chernyauskas-vladislav@yandex.ru, ORCID: https:\/\/orcid.org\/0009-0002-8438-3418, SPIN-code: 5867-6366
    Andrey V. Brailovsky<\/strong>, Assistant Professor, Department of Digital Transformation, Institute of Information Technologies, 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
    Roman N. Rezenkov<\/strong>, Candidate of Technical Sciences, Associate Professor, Associate Professor at the Digital Transformation Department, Institute of Information Technologies, MIREA \u2013 Russian Technological University;
    78, Vernadsky prospekt, Moscow, 119454, Russia;
    rezenkov@mirea.ru, ORCID: https:\/\/orcid.org\/0009-0005-5542-2125<\/p>\n\n\n\n

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

    The study was performed without external funding.<\/p>\n<\/details>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Hourly load forecasting via PatchTST and Temporal Fusion Transformers: comparative system analysis with CatBoost and N-HiTS network models A.E. Dzgoev, E.V. Klimkin, V.V. Chernyauskas, A.V. Brailovsky, R.N. Rezenkov Abstract. Under modern conditions of dynamic demand behavior and external factors, traditional statistical autoregressive modeling methods (ARIMA, SARIMAX) are increasingly outperformed by machine learning algorithms (Gradient Boosting) […]<\/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-15405","page","type-page","status-publish","hentry"],"yoast_head":"\n28.3.4 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\/28-3-4-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"28.3.4 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=\"Hourly load forecasting via PatchTST and Temporal Fusion Transformers: comparative system analysis with CatBoost and N-HiTS network models A.E. Dzgoev, E.V. Klimkin, V.V. Chernyauskas, A.V. Brailovsky, R.N. Rezenkov Abstract. Under modern conditions of dynamic demand behavior and external factors, traditional statistical autoregressive modeling methods (ARIMA, SARIMAX) are increasingly outperformed by machine learning algorithms (Gradient Boosting) […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-3-4-en\/\" \/>\n<meta property=\"og:site_name\" content=\"\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=\"article:modified_time\" content=\"2026-06-30T08:59:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/03\/image.png\" \/>\n\t<meta property=\"og:image:width\" content=\"80\" \/>\n\t<meta property=\"og:image:height\" content=\"28\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041f\u0440\u0438\u043c\u0435\u0440\u043d\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0434\u043b\u044f \u0447\u0442\u0435\u043d\u0438\u044f\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 \u043c\u0438\u043d\u0443\u0442\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/izvestiyakbncran.ru\\\/index.php\\\/en\\\/28-3-4-en\\\/\",\"url\":\"https:\\\/\\\/izvestiyakbncran.ru\\\/index.php\\\/en\\\/28-3-4-en\\\/\",\"name\":\"28.3.4 En - 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