Data aggregation and sanitization: The key to optimal solar plant performance

Informed decision-making requires more than an accumulation of data. Solar plants generate an extraordinary amount of data — about topics ranging from energy production to equipment health — but that data isn’t useful unless organized and expressed in a digestible (and accurate) format.

Data aggregation brings together diverse data streams and provides a big-picture view of plant operations. Sanitization logically improves the accuracy and precision of aggregated data, eliminating inconsistencies that can distort PV plant performance and decision-making. ?

Data aggregation for enhancing insight and efficiency

Data aggregation, in this context, is the process of gathering and organizing a myriad of data points from various sources within a solar plant. The idea is to compile and summarize data so it can be more easily understood.?

Solar PV systems are complex ecosystems. Panels measure energy production, inverters monitor conversions, sensors track temperature and humidity, and weather stations capture atmospheric conditions. This multi-dimensional data landscape is a treasure trove of information — but a challenge to manage effectively. Data aggregation brings these diverse data streams together in a more coherent narrative. ?

Data sanitization for better decision-making

Beyond collecting and organizing the massive amount of data produced by solar systems, there’s a bigger issue at play. Data collected at solar plants is often of very poor quality. This happens for a number of reasons, including faulty equipment, misplaced sensors, or particularly frustrating — inaccurate timestamps.?

Without the proper strategies in place to deal with these typical data issues (outliers, offsets, gaps, stuck values, etc.) any analytics based on such data is doomed to have problematic results.

Data sanitization eliminates inaccuracies, inconsistencies, and redundancies, ensuring that collected data is free of errors that could potentially skew analysis and decision-making.

Inconsistencies are identified and adjusted through a rigorous process of data validation, rectification, and when necessary, enhancement. When data is cleansed in this way, insights drawn from it are sharper, more reliable, and can act as a solid foundation for strategic planning and preventive maintenance efforts.

You could have the best ML model in the world, but if you feed it unsanitized data, you’ll get suboptimal results at best and useless results at worst. As the saying goes, “garbage in, garbage out.”

But with well-sanitized data, you can empower better-informed decisions to drive efficiency and performance. You’ll gain reliable insights, which lead to more accurate predictions — enabling operators to proactively address potential issues and optimize energy generation. Whether it's scheduling maintenance, predicting equipment failures, or identifying areas for performance enhancement, sanitized data provides the necessary clarity and precision for effective planning.

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Achieving data excellence with AI-powered solar software

As solar plant operations evolve in complexity and scale, support that goes beyond human limitations is needed to harness the full potential of available data.

Artificial intelligence (AI) is transforming data management. Powered by advanced machine learning algorithms, AI processes and categorizes data much more efficiently than humans, greatly improving both the speed and scale at which data can be managed.

While manual analysis can identify obvious correlations, AI's keen eye can spot nuances that would likely escape human observation. Anomalies that would typically be lost in a vast sea of data are meticulously uncovered.

Insights derived from AI-processed data empower solar plant operators to make proactive choices that optimize performance. From predicting equipment failures to recommending maintenance schedules based on use patterns, AI opens doors to a new era of precision and efficiency in solar plant operations. ?

The role of data mastery in autonomous operations

Data aggregation and sanitization set the stage for a key function of raicoon autonomous operations center (AOC): Automated fault detection.

raicoon AOC first gathers and standardizes raw data, and then sanitizes it by using AI and machine learning algorithms to transform noisy or poor-quality data into information that can be used quickly and efficiently. By doing so, the AOC is able to make informed decisions, detect measurable faults with 100% accuracy, and effectively run PV plants on autopilot.

Interested in achieving data mastery and running your PV plant on autopilot with AI-powered solar software? The journey toward optimal performance begins with organized and sanitized data. Schedule a call with our team to learn more about the impact of data quality in improving your operations.