ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-2-374-381ntv-383Research ArticleИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И КОГНИТИВНЫЕ ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИARTIFICIAL INTELLIGENCE AND COGNITIVE INFORMATION TECHNOLOGIESМодель аналитики энергопотребления на основе интеллектуальной оболочки Game Optimization для данных интеллектуального учетаAn intelligent shell game optimization based energy consumption analytics model for smart metering datahttps://orcid.org/0000-0003-3503-1133СаравананР.SaravananR.

Сараванан Рамалингам — PhD, доцент

Пудучерри, 605107sc 57211236494

Ramalingam Saravanan — PhD, Associate Professor

Puducherry, 605107sc 57211236494

saravanan@smvec.ac.inhttps://orcid.org/0000-0001-7672-1339СваминатанА.SwaminathanA.

Сваминатан Арулнантишивам — PhD, доцент

Ченнаи, 600123

sc 55624126400

Arulnanthisivam Swaminathan — PhD, Associate Professor

Chennai, 600123

sc 55624126400

swamisivam19@gmail.comhttps://orcid.org/0000-0002-9013-9801БаладжиС.BalajiS.

Баладжи Санкаралингам — PhD, доцент

Пудучерри, 605107sc 55310821000

Sankaralingam Balaji — PhD, Associate Professor

Puducherry, 605107sc 55310821000

balaji@smvec.ac.inИнженерный колледж Шри Манакулы Винаягар ПудучерриИндияSri Manakula Vinayagar Engineering CollegeIndiaПанималарский инженерный колледжИндияPanimalar Engineering CollegeIndia202321122024232374381Copyright © Сараванан Р., Сваминатан А., Баладжи С., 20242024Сараванан Р., Сваминатан А., Баладжи С.Saravanan R., Swaminathan A., Balaji S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/383

Интеллектуальные измерения привлекают к себе все большое внимание из-за ненадежности электромеханических измерений, больших затрат труда и времени. Существующие методы прогнозирования сосредоточены на работе с данными и не уделяют должного внимания полученным результатам. Точное прогнозирование потребления электроэнергии позволяет предоставлять услуги по планированию ресурсов, контролю действия по балансированию спроса и предложения. Пользователи получают выгоду при применении интеллектуального учета за счет эффективной интерпретации результатов использования энергии и благодаря экономичному) управлению затратами на электроэнергию. В работе представлена интеллектуальная аналитика энергопотребления с применением модели данных интеллектуального учета ECA-SMD для определения использования энергии. Модель включает предварительную обработку данных, извлечение признаков, классификацию и оптимизацию параметров. Использована классификация на основе машин экстремального обучения (Extreme Learning Machine, ELM) для определения оптимальных меток классов. Применен алгоритм оптимизации Shell Game Optimization для настройки параметров, участвующих в ELM и повышения эффективности классификации. Работоспособность модели ECA-SMD проверена с использованием обширного набора данных интеллектуальных измерений. Предложенная модель показала максимальную точность 65,9 % и среднеквадратичное отклонение 0,096.

Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time. All the existing methods are focused only on how to deal with data rather than how to do efficiently. Prediction of electricity consumption is essential to gain intelligence to the smart gird. Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing. The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost. With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy. The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization. The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels. Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency. The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE). The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096.

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