فصلنامه سیستم های انرژی پایدار

فصلنامه سیستم های انرژی پایدار

مدل ترکیبی نوآورانۀ تعیین پایداری شبکه و همترازی انرژی الکتریکی در بخش صنعتی با رویکرد یادگیری ماشین داده‌محور

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری مدیریت سیستم‌ها، دانشکدۀ مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران
2 استادیار گروه مدیریت صنعتی و فناوری، دانشکدۀ مدیریت و حسابداری دانشکدگان فارابی، دانشگاه تهران، قم، ایران
3 دانشیار گروه مدیریت صنعتی و فناوری، دانشکدۀ مدیریت و حسابداری دانشکدگان فارابی، دانشگاه تهران، قم، ایران
10.22059/ses.2026.415044.1247
چکیده
با توجه به ضرورت گذار به انرژی‌های تجدیدپذیر و اهمیت پایداری شبکۀ برق سراسری، این پژوهش با هدف تحلیل شاخص‌های کمّی مؤثر بر همترازی تولید و مصرف انرژی الکتریکی در بخش صنعت انجام شده است. نوآوری اصلی مقاله، ارائۀ نوعی پلتفرم تحلیل داده‌محور مبتنی بر مدل‌های هوشمند است که شکاف میان مبانی نظری انرژی پایدار و تحلیل داده‌های واقعی را پوشش می‌دهد. در این مطالعه، از ترکیب الگوریتم‌های طبقه‌بندی و خوشه‌بندی شامل جنگل تصادفی، ماشین بردار پشتیبان و خوشه‌بندی K-Means استفاده شد. همچنین برای بهبود اعتبارسنجی، دو مدل ترکیبی شامل جنگل تصادفی ـ ماشین بردار پشتیبان و خوشه‌بندی ـ ماشین بردار پشتیبان با استفاده از محیط پایتون و تنظیم فراپارامترها توسعه یافت. داده‌های کمّی مورد بررسی شامل مصرف انرژی الکتریکی در بخش صنعتی، تولید انرژی تجدیدپذیر، قیمت انرژی الکتریکی و مصرف کل برق کشور است. نتایج نشان می‌دهد تولید انرژی تجدیدپذیر روندی افزایشی داشته و افزایش قیمت انرژی، صنایع را به سمت تولید برق در محل کارخانه سوق داده است. نتایج خوشه‌بندی در پنج خوشه نیز بیانگر رشد متناسب تولید انرژی تجدیدپذیر است؛ به‌ گونه‌ای که تولید روزانه آن حداقل 10 درصد مصرف برق کشور را تشکیل می‌دهد. بر اساس معادلۀ همترازی انرژی الکتریکی، راهکارهایی مانند قیمت‌گذاری زمان‌محور، مدیریت هوشمند انرژی، سرمایه‌گذاری در ذخیره‌سازی برق و اعمال مالیات کربن پیشنهاد شده است. این مقاله با افزایش دقت تحلیل داده‌های کلان انرژی، نقشۀ راهی عملیاتی برای پایداری شبکۀ برق و مشارکت مستقیم صنایع در تولید انرژی پاک ارائه می‌کند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

An Innovative Hybrid Model for Determining Grid Stability and Electrical Energy Alignment in the Industrial Sector Using a Data-Driven Machine Learning Approach

نویسندگان English

Ali Alam 1
Mahsa Pishdar 2
Mohamad Reza Fathi 3
1 PhD Candidate in Systems Management, Faculty of Management, College of Farabi, University of Tehran, Qom, Iran Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
2 Assistant Professor, Department of Industrial Management and Technology, Faculty of Management, College of Farabi, University of Tehran, Qom, Iran
3 Associate Professor, Department of Industrial Management and Technology, Faculty of Management, Colleg of Farabi, University of Tehran, Qom, Iran
چکیده English

Given the necessity of transitioning to renewable energies and the importance of the stability of the national power grid, this study was conducted with the aim of analyzing the quantitative indicators affecting the alignment of electrical energy generation and consumption in the industrial sector. The main innovation of the paper is the presentation of a data-driven analytical platform based on intelligent models, which bridges the gap between the theoretical foundations of sustainable energy and the analysis of real data. In this study, a combination of classification and clustering algorithms, including Random Forest, Support Vector Machine, and K-Means clustering, was used. Furthermore, to improve validation, two hybrid models, including Random Forest–Support Vector Machine and Clustering–Support Vector Machine, were developed using the Python software environment and hyperparameter tuning. The quantitative data examined include electrical energy consumption in the industrial sector, renewable energy generation, electricity price, and total electricity consumption in the country. The results show that renewable energy generation has followed an increasing trend, and that rising energy prices have encouraged industries to move toward on-site electricity generation at factories. The results of clustering into five clusters also indicate proportional growth in renewable energy generation, such that its daily generation accounts for at least ten percent of the country’s electricity consumption. Based on the electrical energy alignment equation, solutions such as time-based pricing, smart energy management, investment in electricity storage, and the implementation of a carbon tax are proposed. By increasing the accuracy of big energy data analysis, this paper provides an operational roadmap for power grid sustainability and the direct participation of industries in clean energy generation.

کلیدواژه‌ها English

Data-driven machine learning
sustainability
Energy balance
renewable electric energy
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