Comparative Analysis of Solar Panel Efficiency Under Different Climatic Conditions

Thesis Abstract

The increasing global reliance on renewable energy sources underscores the importance of optimizing solar photovoltaic (PV) system performance across diverse climatic regions. Despite the proliferation of solar panel installations worldwide, variations in efficiency attributable to climatic factors such as temperature, humidity, solar irradiance, and atmospheric conditions remain inadequately quantified, leading to challenges in effective system deployment and performance prediction. This study aims to conduct a comparative analysis of solar panel efficiency across three distinct climatic zones—tropical, temperate, and arid—located within the same country, with the ultimate goal of elucidating how local environmental conditions influence PV system performance. The specific objectives include measuring and comparing the instantaneous and cumulative efficiencies of identical monocrystalline silicon panels deployed in these zones over a 12-month period, analyzing the correlation between climatic variables and efficiency metrics, and developing a predictive model to estimate panel performance based on local environmental parameters. The research adopts a quantitative, cross-sectional descriptive design, integrating field measurements with statistical analysis to examine efficiency variations across climates. The study population comprises three solar PV installations—one in each climatic zone—each consisting of identical 300-watt monocrystalline silicon panels mounted on fixed tilt structures. A total of nine panels (three in each region) serve as the sampling units, with data collected continuously via a combination of pyranometers, temperature sensors, humidity gauges, and digital data loggers. These instruments record solar irradiance, ambient temperature, panel temperature, humidity levels, and electrical output at 15-minute intervals. The data collection process spans an entire year, ensuring capture of seasonal variations. The validity and reliability of measurement instruments are enhanced through calibration against standard references and repeated measurements during different weather conditions. Data analysis involves descriptive statistics to summarize efficiency metrics, while inferential analysis utilizes one-way ANOVA to identify significant differences in efficiency among the climatic zones. Multiple regression analysis is employed to determine the extent to which climatic variables predict PV efficiency, guided by the Theory of Solar Radiation and the Atmosphere, which posit that solar energy conversion efficiency is modulated by environmental factors. Spatial and temporal data are processed using statistical software such as SPSS and R, with the development of a multivariate predictive model for site-specific efficiency estimation. Expected findings include statistically significant variations in solar panel efficiency across the different climatic conditions, with tropical zones exhibiting higher efficiency due to consistent solar irradiance but experiencing declines owing to high temperatures, while arid zones show optimal efficiency owing to high irradiance with lower humidity effects. The regression analysis is anticipated to reveal temperature and humidity as key predictors of efficiency fluctuations. Furthermore, the study aims to establish region-specific models that accurately forecast PV performance, contributing valuable insights for system design and optimal siting. This research contributes to the existing body of knowledge by systematically quantifying the environmental influences on solar panel efficiency, providing empirical evidence to inform regionalized deployment strategies, and enhancing predictive modeling capabilities for PV system performance assessment. The findings are expected to facilitate improved planning, economic viability, and environmental sustainability of solar energy projects, particularly in regions with diverse climatic profiles. Based on these results, final recommendations advocate for climate-adaptive solar panel technology, site-specific performance optimization, and policy frameworks to support data-driven renewable energy scaling. The study also suggests avenues for future research, including the exploration of advanced cooling technologies and integrating climate resilience considerations into PV system design.