energyЭнергетика. Известия высших учебных заведений и энергетических объединений СНГENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations1029-74482414-0341BNTU10.21122/1029-7448-2026-69-1-5-22energy-2532Research ArticleОБЩАЯ ЭНЕРГЕТИКАGENERAL POWER ENGINEERINGПовышение качества подбора энергосберегающих мероприятий зданий при использовании искусственных нейронных сетейImproving Performance of Fitting Energy-Saving Measures of Buildings Using Artificial Neural NetworksСтанВ. К.StanV. K.

Адрес для перепискиСтан Василий КонстантиновичБелгородский государственный технологический университет имени В. Г. Шуховаул. Костюкова, 46,308012, г. Белгород, Российская ФедерацияТел.: +7 (4722) 30-99-64

madseal@yandex.ru

Address for correspondence

Stan Vasilу K.Belgorod State Technological University named after V. G. Shukhov46, Kostyukova str.,308012, Belgorod, Russian FederationTel.: +7 (4722) 30-99-64

madseal@yandex.ru

madseal@yandex.ruБухановД. Г.BukhanovD. G.

г. Белгород

Belgorod

КошличЮ. А.KoshlichYu. A.

г. Белгород

Belgorod 

Белгородский государственный технологический университет имени В. Г. ШуховаРоссияBelgorod State Technological University named after V. G. ShukhovRussian Federation202610022026691522Copyright © Стан В.К., Буханов Д.Г., Кошлич Ю.А., 20262026Стан В.К., Буханов Д.Г., Кошлич Ю.А.Stan V.K., Bukhanov D.G., Koshlich Y.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://energy.bntu.by/jour/article/view/2532

Статья посвящена повышению энергосбережения и энергоэффективности учреждений бюджетной сферы за счет автоматизации процесса подбора энергосберегающих мероприятий. В качестве технологии подбора мероприятий в работе предложен классификатор, построенный на базе искусственной нейронной сети. Набор информационных признаков дополнен категориальными данными зданий учреждений. Актуальность работы обоснована необходимостью разработки решений, направленных на внедрение мер по повышению энергосбережения и энергоэффективности в бюджетной сфере. Описаны структура и принцип работы модуля автоматического подбора мероприятий по энергосбережению в составе системы управления энергетическими ресурсами (СУЭР). В работе произведены исследования качества подбора энергосберегающих мероприятий с наличием и отсутствием категориальных признаков. Произведен анализ выделенных категориальных признаков, а также сравнительный анализ методов их кодирования. Предложена методика оценки точности работы классификатора в контексте решаемой задачи. Путем перебора комбинаций методов преобразования количественных и категориальных данных проведен ряд экспериментов с целью определения значимости категориальных признаков в целом, а также определения наиболее результативного сочетания методов их кодирования. Произведен сравнительный анализ полученных результатов с определением наиболее успешной модели, для которой была произведена дополнительная оценка качества работы на базе метрик точности, отклика и средневзвешенной F-меры. Сделаны выводы о целесообразности дополнения исходного набора категориальными данными для улучшения показателей работы разработанной системы.

The article is devoted to increasing energy saving and energy efficiency of public sector institutions by automation of the process of energy-saving measures fitting. A classifier based on an artificial neural network is proposed as a technology for selecting measures. The base numeric dataset was supplemented by categorical data on buildings and structures. The urgency of the work is justified by the need to develop solutions aimed at introducing measures to ensure energy saving and energy efficiency in the public sector. The structure and operating principle of a module of auto-selecting energy saving measures as a part of the Energy Resources Management System (SUER) are described. The work studies the quality of fitting energy-saving measures with or without using data on categorical features. An analysis of the selected categorical features, as well as a comparative analysis of their encoding methods, is carried out. An accuracy evaluation method of the classifier in the context of the work is proposed. A series of experiments were conducted by enumerating combinations of numeric and categorical data converting methods, in order to determine the significance of categorical features in general, as well as to determine the most effective combination of encoding methods. A comparative analysis of the results of the experiments was performed and the most successful model was determined, with an additionnal assessment of the quality of the model was made based on the metrics of precision, recall and F1-score. Conclusions were made on the advisability of supplementing the original dataset with categorical data to improve the performance of the system.

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The authors declare that there are no conflicts of interest present.