A Model System: Utilizing Machine Learning for Data Analysis
Data analysis is crucial to the operation of solar power plants, as the resulting information provides unmatched insight into how a plant is performing. Advanced analytics takes this a step further: by formulating statistical matrices, implementing actionable performance alarms, and conducting artificial intelligent models, site operators are able to optimize production performance and consult historical data to plan ahead. Trimark’s SCADA system does all of this and more, incorporating advanced analytics, machine learning, and deep learning models to revolutionize how owners and operators manage their renewable energy assets.
A Step Ahead
Critical to this goal is the proper evaluation of Key Performance Indicators (KPIs). One such KPI is the Real-Time Weather Adjusted Performance Index, which measures properties including irradiance, back panel temperature, power generation, DC and AC capability, and more. Reviewing these KPIs affords engineers a number of conveniences, including the abilities to proactively troubleshoot sites and accurately forecast future performance based on historical data.
These predictions are invaluable, as they allow site operators and engineers to take preliminary measures that maximize energy production, while minimizing device failures. Regarding energy production, many of these predictions rely on machine learning (ML) models, which, as “learning” implies, become more accurate with each application.
Seeing the Forest and the Trees
The Random Forest model is one such example, which Trimark employs to predict daily energy generation. Per its name, the Random Forest model randomly splits the data features into subsets of trees, thereby increasing the model’s accuracy. While this accuracy deteriorates as more days are forecasted, the model gives owners and operators a pretty good idea of what the first three days will look like, enabling them to plan ahead.
Imitation is the Highest Form of Flattery
Another popular ML model used by Trimark is the Artificial Neural Networks (ANN) model, which boasts superior energy performance predictions. This is due to its emulation of the human brain: by utilizing deep learning algorithms, such as the Multilayer Perceptron (MLP), the ANN model seeks to mimic the functionality of our biological minds, particularly how we perceive subjects and behaviors. To this end, the MLP outputs results based on layers of input, resulting in more accurate energy predictions. Moreover, the ANN model is capable of performing feature engineering on its own. Even with no information regarding the conditions – such as whether the site is covered by snow, soiled, or shaded – ANN is able to learn through layers of information within the data.
No One’s Perfect
However, like the Random Forest model, ANN is not perfectly accurate, as it applies a Point-to-Point energy prediction algorithm that inevitably creates distortions within data results. For example, a dip in production during a cloudy day will be reflected in the following day’s forecast, even if the skies are perfectly clear. Sequence-to-Point (S2P) modeling overcomes this drawback by using several consecutive data points to predict a single future value, rather than just one, resulting in a prediction with fewer aberrations.
Regardless of which model is utilized, Trimark continues to leverage all forms of advanced analytics and machine learning to proactively ensure the operational condition of site devices. Doing so will drastically reduce the length of periods of downtime, greatly limit the amount of lost energy, and consequently increase the lifetime of solar assets. As our SCADA databases contain vast quantities of “learning material” (in the form of detailed fault history information), Trimark is undoubtedly well-equipped to develop, facilitate, and apply advanced machine learning and modeling strategies.
For more information on how Trimark is leveraging machine and deep learning to advance site data analytics, email firstname.lastname@example.org to receive a copy of our white paper on this topic.