Module Underperformance – Part 1: The Sun’s Potential

Modules may underperform due to a range of environmental and non-environmental factors. While good design and operational practices can help address non-environmental issues, the environment remains beyond our control. Although we cannot directly influence the environment, we can study its patterns and assess how they might impact system performance. This is particularly crucial for owners and financiers. Understanding the real risks—rather than relying solely on average predictions from models—can provide deeper insights into a system's true potential. This knowledge is vital for both initial modeling and ongoing monitoring of solar installations. Environmental factors influencing solar performance can be categorized into two main areas: **Part 1 – The Sun’s Potential (Weather Data)** Weather data, often referred to as "solar irradiance data," comes from multiple sources like NREL NSRDB, NASA, Meteonorm, and third-party satellite providers such as SolarAnywhere. These datasets serve as the foundation for predicting solar potential and play a significant role in determining model accuracy. However, these datasets are sometimes derived from various sources and may not perfectly align with recent weather trends. To manage risks effectively, there are several ways we can evaluate the reliability of our weather data: 1. **Historical Analysis**: By comparing weather data against historical records independently of modeling tools like PVSYST, we can calculate a confidence level. This allows us to adjust the data for greater accuracy. For example, reviewing 40 years of weather data from an NSRDB TMY dataset provides a robust statistical foundation. [Insert image: A graph showing 40 years of weather confidence for NSRDB TMY data] 2. **PVSYST Confidence Tools**: PVSYST offers tools to generate standard (P50) and higher confidence (P90-P99) models based on the input data. These tools help in assessing the variability in performance predictions. [Insert image: A chart illustrating P50 and P99 confidence levels] 3. **Comparative Analysis**: We can compare the outcomes from the raw weather data analysis with those from PVSYST. If the PVSYST results fall below the confidence bounds established earlier, it indicates a conservative model. Conversely, if they exceed the bounds, additional adjustments should be made to account for uncertainties when projecting long-term performance. [Insert image: A diagram showing the comparison between raw data analysis and PVSYST outputs] 4. **Performance Verification**: Actual system performance can also be compared to the initial model using a weather-adjusted ratio. This helps validate the model’s accuracy and identifies any discrepancies. [Insert image: A graph displaying the comparison between predicted and actual system performance] **Who Conducts These Complex Tests?** At Pure Power, our Independent Engineering team specializes in performing these intricate assessments, including weather data evaluations and historical performance reviews. Our expertise ensures that you receive reliable insights into your solar projects. If you’re planning your next solar initiative and want to minimize risks while saving time and resources, feel free to reach out to Pure Power. Let us bring clarity and confidence to your renewable energy ventures! [Contact information for Pure Power]

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