ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS FOR SUSTAINABLE POWER GENERATION
DOI:
https://doi.org/10.69980/7qq8ew87Keywords:
Artificial intelligence, renewable energy systems, sustainable power generationAbstract
Artificial intelligence-driven optimization of renewable energy systems offers a practical approach for improving sustainable power generation under variable operating conditions. Renewable energy systems often face challenges related to fluctuating solar irradiance, changing temperature conditions, unstable load demand, storage limitations, and grid dependency. Primary data were collected from a renewable energy setup consisting of solar photovoltaic modules, battery storage, an inverter, load monitoring units, smart meters, and environmental sensors. Python software was used for data preprocessing, AI model development, training, validation, testing, and performance evaluation. The AI model was designed to forecast renewable energy output, optimize energy distribution, improve battery scheduling, and balance power supply with load demand. The results showed that the model achieved strong forecasting performance, with a testing prediction accuracy of 95.24%, a Mean Absolute Percentage Error of 4.76%, and an R² value of 0.938. After AI optimization, average power output increased from 3.48 kW to 3.92 kW, energy efficiency improved from 78.35% to 87.42%, and renewable energy utilization increased from 74.80% to 88.60%. Energy loss decreased by 42.99%, while grid dependency declined by 45.44%. The findings indicate that AI-driven optimization can improve energy efficiency, storage performance, system reliability, cost effectiveness, and environmental sustainability in renewable energy systems.
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