Photovoltaic (PV) systems are key to the transition toward clean energy, but their real-world performance is shaped by complex environmental interactions. For researchers working on the physics of solar energy systems, understanding how variables like temperature, humidity, aerosols, and wind impact panel efficiency provides a critical foundation for deeper modeling and analysis.
This blog post synthesizes environmental insights to support further research in feature selection, sensitivity analysis, and data-driven modeling methods.
Environmental Factors Affecting PV Performance
Photovoltaic systems are influenced by a variety of external conditions that affect energy yield. The table below summarizes the most significant factors.
| # | Factor | Typical Impact | Description |
| 1 | Rainfall | Positive | Can clean soiled panels, reducing optical losses |
| 2 | Temperature | Negative | Reduces solar panel efficiency by lowering the voltage and overall power output |
| 3 | Dust on or soiled panels | Negative | Blocks light and cause thermal stress and uneven shading |
| 4 | High humidity | Negative | Promotes corrosion, leakage and spectral absorption loss |
| 5 | Shading (including clouds) | Negative | Blocks or diffuses sunlight, reducing the direct irradiance reaching panels |
| 6 | Smoke from fires or biomass | Negative | Scatters and absorbs sunlight, reduces irradiance and spectral quality. Sources include wildfires, agricultural burning and landfill combustion. |
| 7 | Aerosols and atmospheric pollutants | Negative | Affect light spectrum and reduce irradiance, particularly in urban and polluted environments |
| 8 | Air pollution | Negative | Lowers irradiance, causes fine soiling and impacts spectrum |
| 9 | Wind | Mixed | Cools module (positive) but also carries particulates that cause soiling (negative) |
| 10 | Snow cover, albedo (surface reflectivity) | Mixed | Snow can block panels (negative) but also enhances ground reflectance (positive) for bifacial modules due to albedo – the reflectivity of surrounding surfaces |
| 11 | Panel tilt or angle | Mixed | Affects solar incidence, soiling behavior (positive and negative) and cleaning via rainfall (positive) |
| 12 | Solar irradiance intensity | Mixed | More sunlight boosts output (positive) but too much causes overheating (negative) |
| 13 | Seasonal variation | Mixed | Changes in weather patterns (temperature, humidity, dust) can cause fluctuations in efficiency (positive and negative) |
| 14 | Combined environmental effects | Mixed | Interaction of multiple factors, such as humidity, dust or wind particulates can compound impacts both positive and negative |
Mitigating Environmental Impacts on Solar Performance
To maintain reliable solar energy output under real-world conditions, it’s essential to apply targeted mitigation strategies that address environmental stressors. Dust accumulation, for example, can drastically reduce panel efficiency, especially in dry climates. Solutions include manual and automated cleaning systems, anti-soiling coatings and panel tilt optimization to minimize dust deposition.
Temperature-related performance loss has prompted research into active cooling systems and heat-resistant materials, which help regulate operating temperatures and sustain output. Humidity presents a more complex challenge by amplifying dust adhesion and promoting corrosion. Protective coatings and ventilation improvements are emerging as viable defenses.
Shading — whether from objects or uneven soiling — can be addressed through smart tracking systems, micro-inverters and layout optimization. Atmospheric pollutants, including smoke and urban aerosols, degrade panel efficiency by altering light transmission. Regular cleaning and surface treatments are critical in these environments.
Effective mitigation often requires integrating multiple strategies. Combining cleaning technologies, thermal management and predictive maintenance tailored to local conditions helps preserve efficiency and reduce downtime. As environmental pressures grow, these strategies are essential for sustaining solar performance and strengthening the reliability of solar-powered grids.
In summary, environmental impacts on photovoltaic performance are highly dependent on local conditions. Factors like dust, humidity, and pollution vary across regions and require localized analysis and mitigation. Some variables—such as wind, irradiance, and snow—have mixed effects that depend on system design and environmental context. These complexities challenge generalization and call for detailed, site-specific modeling.
Recognizing these variations helps researchers identify relevant input features for sensitivity analysis, PCA, and machine learning models. These tools support better solar forecasting, more resilient grid operations, and regionally optimized solar system designs.
