GroundWork Renewables

Lawrence Pratt is a solar analytics and engineering leader with over two decades of experience in photovoltaics. He specializes in performance data, system optimization and advanced testing, driving innovation in solar PV efficiency, reliability and large-scale energy solutions.

Experience Shaping Data-Driven Solar Approach

My approach to leveraging data and analytics in solar performance management has been shaped by my career spanning applied statistics, computer science, advanced manufacturing, and renewable energy.

I began during my MSc in Applied Statistics and Mathematics at the University of New Mexico, and worked as a Graduate Research Assistant at Los Alamos National Laboratory. There, I applied statistical methods to environmental datasets, building a strong foundation in data-driven problem solving.

I later joined Intel Corporation as a Yield Engineer, using statistical process control and design of experiments to improve product quality, reduce costs, and increase throughput. Intel’s culture of data-driven decision-making in a high-volume manufacturing environment had a lasting influence on how I approach variability, systems, and continuous improvement.

“I view solar plants as distributed manufacturing systems designed to produce electrons at scale.”

Since entering the solar industry in 2006, I’ve applied those same principles to PV performance. I view solar plants as distributed manufacturing systems designed to produce electrons at scale. This perspective drives my focus on using data to diagnose performance issues, reduce variability, and optimize output across fleets.

Key Metrics for Solar Performance

My work has focused on linking initial module performance to long-term energy yield.

At the module level, this begins with accurate power measurements and controlled testing to establish a reliable baseline. Long-term reliability metrics—such as degradation rates—are essential for predicting performance over time.

In operating plants, I focus on the temperature-corrected performance ratio (PR) of the module and string level DC output as measured in situ by our company’s custom edge devices. This enables normalization for environmental conditions and helps detect subtle shifts that are often masked in system-level data.

I also track:

• Key IV parameters (Pmp, Imp, Vmp, Voc, Isc)

• Degradation trends over time

• Variability across strings and arrays

• Data quality and uncertainty

These metrics support early detection, root-cause diagnostics, and proactive optimization.

Challenges in Maintaining Data Accuracy

Maintaining accurate and reliable performance data at utility scale is challenging.

First, scale limits direct measurement, forcing reliance on aggregated data that can obscure localized issues.

Second, environmental variability—irradiance, temperature, soiling, and spectral effects—makes it difficult to distinguish real performance changes from normal operating conditions.

Third, operational factors such as curtailment and downtime distort performance signals and can be misinterpreted if not properly accounted for.

“I view solar plants as distributed manufacturing systems designed to produce electrons at scale.”

Additionally, data quality issues—including sensor drift, data gaps, communication failures, and time misalignment—reduce confidence in analysis.

Finally, reliance on inverter-level data limits visibility into module- or string-level behavior, delaying detection of emerging issues.

Addressing these challenges requires high-quality measurements from in-situ hardware, robust data engineering, and analytics that normalize for conditions and enable asset-level insights.

Data and Analytics Driving Solar Scale

As the industry scales toward gigawatt-level deployments, data and analytics are becoming central to performance, reliability and financial outcomes.

Machine learning is enabling a shift from reactive to predictive and increasingly autonomous asset management. These methods are evolving to handle the growing volume, velocity, and variety of data, while improving data quality and decision value.

We are seeing tighter integration of high-frequency operational data, edge devices communicating directly to the cloud, physics-based models, and AI-driven diagnostics to improve energy yield and reduce downtime. In PV, combining IV curve analytics, weather data, and fleet-wide benchmarking can unlock significant performance gains.

Career Advice for Energy Analytics

Develop strength in both data science and domain knowledge.

Build a solid foundation in statistics, data analysis, and programming, but also understand how energy systems operate in solar, which includes PV modules, inverters and environmental drivers of performance.

Focus on connecting analytics to real-world outcomes—improving yield, reducing downtime, and lowering cost.

Be comfortable working with imperfect data, and develop the ability to assess data quality and uncertainty.

Adopt a systems-level mindset, integrating multiple data sources to generate meaningful insights.

Finally, stay adaptable. The field is evolving rapidly, and continuous learning is essential.