Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management
Khababa, G., Blida, K., Khababa, A. (2024). Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management. EKB Journal Management System, 7(1), 185-194. doi: 10.21608/ijsrsd.2024.396829
Ghizlane Khababa; Kamilia Blida; Abdallah Khababa. "Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management". EKB Journal Management System, 7, 1, 2024, 185-194. doi: 10.21608/ijsrsd.2024.396829
Khababa, G., Blida, K., Khababa, A. (2024). 'Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management', EKB Journal Management System, 7(1), pp. 185-194. doi: 10.21608/ijsrsd.2024.396829
Khababa, G., Blida, K., Khababa, A. Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management. EKB Journal Management System, 2024; 7(1): 185-194. doi: 10.21608/ijsrsd.2024.396829

Leveraging Machine Learning for Sustainable Solar Power: Techniques for Enhanced Generation and Management

International Journal of Sustainable Development and Science
Volume 7, Issue 1, 2024, Page 185-194  XML PDF (681.07 K)
Document Type: Original Article
DOI: 10.21608/ijsrsd.2024.396829
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Authors
Ghizlane Khababa email 1; Kamilia Blida2; Abdallah Khababa3
1LRSD Laboratoire des résaux et systèmes distribués, Department of Computer Science, Faculty of Sciences, University Sétif 1-Algeria
2Faculty of Economics, University El Bachir El Ibrahimi-Bordj Bou Arreridj
3Department of Computer Science, Faculty of Sciences, University Sétif
Abstract
The paper in hands presents a Long Short-Term Memory (LSTM) model to forecast solar power generation and management, as it aiming to improve the reliability and efficiency of solar energy systems. LSTM, a type of recurrent neural network, is well-suited for handling time series data and capturing long-term dependencies, making it an effective tool for predicting fluctuations in solar power generation due to variable weather patterns and seasonal changes. By utilizing historical weather data, irradiance levels, and past solar output, the LSTM model predicts short-term and medium-term solar power generation, allowing for optimized energy management and improved grid integration. This model helps address challenges in balancing demand and supply, reducing reliance on fossil fuels, and enhancing the sustainability of renewable energy sources. The results indicate that the LSTM-based forecasting model achieves high accuracy, significantly reducing prediction errors compared to traditional forecasting methods, thereby supporting more efficient solar power management strategies.
Keywords
Solar power Forecasting; LSTM ( Long Short Term memory); Renewable Energy Prediction; Deep Learning for Solar Energy
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