An assessment of solar power generating system as a solution to deal with global warming and climate change

• Autorzy: Salman Ahmed S. , Deifalla Ahmed F. , Atamurotov Farruh

Identyfikatory

DOI

10.24425/jwld.2025.154257

• Języki publikacji: EN

Abstrakty

EN

The increasing adoption of solar power as a sustainable energy source necessitates more efficient and reliable methods for optimising and maintaining solar power generating systems. Traditional approaches to assessing and managing these systems often rely on static models and manual interventions, which can be inefficient and fail to account for dynamic environmental conditions. In this study, we propose a novel framework for the assessment and optimisation of solar power systems using modern machine learning techniques. Our approach benifits advanced predictive maintenance, real-time energy yield optimisation, and enhanced energy forecasting models, resulting in significant improvements in system efficiency and reliability. Specifically, the predictive maintenance system, driven by machine learning algorithms, was able to reduce system downtime by 29.88% compared to traditional reactive maintenance methods. The real-time energy yield optimisation, leveraging dynamic data inputs, increased energy capture efficiency by 14.78% over standard static models. Additionally, our enhanced energy forecasting models demonstrated a 25.12% improvement in accuracy, significantly outperforming conventional forecasting techniques. These innovations enhance the operational efficiency of solar power systems, and contribute in their long-term sustainability and economic viability. The integration of machine learning into solar power management enables proactive decision-making, adaptive control strategies, and more accurate performance predictions. As a result, our proposed framework offers a practical and scalable solution to meet the growing demands of the renewable energy sector and supports the global transition toward cleaner and more resilient energy infrastructures.

Słowa kluczowe

EN

climate change; energy forecasting; machine learning; predictive maintenance; solar power; system optimisation

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2025
• Tom: no. 65
• Strony: 133--140
• Opis fizyczny: Bibliogr. 23 poz., tab., wykr.

Twórcy

autor

Salman Ahmed S.

• Northern Technical University, Oil and Gas Technologies Engineering College, Baghdad St, 315/4, Kirkuk, 36001, Iraq

• https://orcid.org/0000-0002-0596-3813

autor

Deifalla Ahmed F.

• Future University in Egypt, South Teseen, Building A, New Cairo, 11835, Egypt

• https://orcid.org/0000-0003-1758-3743

autor

Atamurotov Farruh

• Inha University in Tashkent, Ziyolilar, 9, Tashkent 100170, Uzbekistan
• Alfraganus University, Yukori Karakamish St, 2a, Tashkent 100190, Uzbekistan
• University of Tashkent for Applied Sciences, Gavhar St, 1, Tashkent, 100149, Uzbekistan

• https://orcid.org/0000-0001-8857-4970

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