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Environmental, Operational, and Systemic Determinants of Photovoltaic Performance: An Integrated Performance–Monitoring and Data-Driven Interpretation Framework
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
The rapid global deployment of photovoltaic (PV) systems has transformed solar energy from a niche renewable technology into a cornerstone of contemporary energy transitions. Despite this growth, persistent discrepancies between predicted and actual PV performance continue to challenge engineers, policymakers, and investors. These discrepancies arise from a complex interplay of environmental conditions, operational practices, system design choices, monitoring methodologies, and data management paradigms. This research article develops a comprehensive, publication-ready scholarly investigation into the determinants of PV system performance by synthesizing environmental, operational, analytical, and data-centric perspectives found within the existing literature. Building upon recent comprehensive reviews of PV performance factors, this study situates environmental stressors such as temperature, irradiance variability, humidity, dust accumulation, and climate-specific degradation within a broader operational and systemic context, including maintenance regimes, grid integration, monitoring standards, and data processing architectures.
The article adopts an interpretive, literature-grounded methodological approach, emphasizing descriptive analytical reasoning rather than mathematical modeling or empirical experimentation. Performance indicators, degradation phenomena, and monitoring metrics are critically examined through internationally recognized standards and long-term field observations. Particular emphasis is placed on the role of standardized monitoring protocols and intelligent data interpretation frameworks in reducing uncertainty and enhancing reliability. The discussion further extends into the often-overlooked intersection between PV performance analysis and database management systems, arguing that the scalability, consistency, and semantic integrity of performance data storage directly influence analytical accuracy and decision-making.
By integrating insights from energy engineering, climate-specific PV studies, monitoring standards, artificial intelligence–based prediction models, and foundational database theory, this article articulates a unified conceptual framework for understanding PV performance as a socio-technical system. The findings underscore that PV performance is not solely a function of module efficiency or environmental exposure, but rather an emergent property shaped by data quality, monitoring fidelity, and interpretive rigor. The article concludes by identifying persistent knowledge gaps, methodological limitations, and future research directions necessary for achieving resilient, high-fidelity PV performance assessment in increasingly complex energy systems (Hasan et al., 2022; Srivastava et al., 2020; IEC, 2021).
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References
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