دورية أكاديمية
Neural network-assisted integration of renewable sources in microgrids: A case study
| العنوان: | Neural network-assisted integration of renewable sources in microgrids: A case study |
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| المؤلفون: | Vladimirovich Kotov Evgeny, Ramesh Banoth |
| المصدر: | MATEC Web of Conferences, Vol 392, p 01172 (2024) |
| بيانات النشر: | EDP Sciences, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Engineering (General). Civil engineering (General) |
| مصطلحات موضوعية: | renewable energy, microgrids, neural network, energy integration, sustainability, Engineering (General). Civil engineering (General), TA1-2040 |
| الوصف: | This study examines the incorporation of renewable energy sources into microgrids using neural network-assisted optimization methods. The objective is to tackle the difficulties related to the fluctuation and uncertainty of renewable energy production. An examination of the collected data over various time periods indicates encouraging patterns in the production of renewable energy. The solar energy use shows a steady rise from 120 kWh to 140 kWh, representing a 16.67% increase. Similarly, wind energy usage also demonstrates an upward trend, increasing from 80 kWh to 95 kWh, marking an 18.75% expansion. The biomass energy production has seen a substantial increase from 50 kWh to 65 kWh, representing a significant 30% rise. The examination of microgrid load consumption demonstrates the increasing energy needs in residential, commercial, and industrial sectors. The household load consumption has increased from 150 kWh to 165 kWh, representing a 10% spike. Additionally, the commercial load and industrial load have also seen a surge of 15%. The predictions made by the neural network demonstrate a high level of accuracy, closely matching the actual output of renewable energy. The accuracy rates for solar, wind, and biomass projections are 98.4%, 95.5%, and 97.3% correspondingly. The assessment of improved energy distribution emphasizes the effective usage of renewable sources, guaranteeing grid stability and optimal resource utilization. The results highlight the capacity of neural network-assisted methods to precisely predict renewable energy outputs and efficiently incorporate them into microgrids, hence promoting sustainable and resilient energy solutions. This report provides valuable insights on improving microgrid operations, decreasing reliance on traditional energy sources, and accelerating the shift towards sustainable energy systems. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English French |
| تدمد: | 2261-236X |
| Relation: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01172.pdf; https://doaj.org/toc/2261-236X |
| DOI: | 10.1051/matecconf/202439201172 |
| URL الوصول: | https://doaj.org/article/7b2a85c21e1e4d52a9516fa37befd42d |
| رقم الأكسشن: | edsdoj.7b2a85c21e1e4d52a9516fa37befd42d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2261236X |
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| DOI: | 10.1051/matecconf/202439201172 |