Интеграция методов машинного обучения в электроэнергетическом секторе для снижения коммерческих потерь: обзор практики в сетевой организации
Аннотация
Внедрение технологии интеллектуальных сетей или интеллектуальных приборов учета (далее – ИПУ) электроэнергии позволяет оптимизировать функционирование энергосистем. Данные, собираемые такими устройствами, позволяют фиксировать потребление энергии каждые полчаса, выявлять случаи низкого качества энергоснабжения, попытки вмешательства в работу приборов учета, а также отслеживать важные события потребления услуг. Несмотря на использование интеллектуального оборудования, проблема неоплаченного энергопотребления остается актуальной даже при наличии системы удаленных измерений. Особенно остро стоят проблемы, связанные с намеренным хищением электрической энергии, которое представляет собой серьезную угрозу для электроснабжающих организаций. Таким образом, задачей исследования стало выявление возможностей применения искусственных нейронных сетей на основании обработки данных, полученных с ИПУ, для анализа данных потребителей и выявления случаев безучетного потребления. Итоговая разработка модели собственной нейросети позволила доказать гипотезу о применимости технологий искусственного интеллекта для борьбы с коммерческими потерями электроэнергии. Анализ результатов показал эффективность сверточных нейронных сетей в классификации временных рядов потребляемой энергии. Это подтверждает потенциал предложенной методики для практической реализации в сфере энергетики, обеспечивая своевременную и надежную информацию для управления ресурсами.
Благодарности
Работа выполнена за счет гранта, предоставленного Академией наук Республики Татарстан образовательным организациям высшего образования, научным и иным организациям на поддержку планов развития кадрового потенциала в части стимулирования их научных и научно-педагогических работников к защите докторских диссертаций и выполнению научно-исследовательских работ.
Скачивания
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