By Karthik Yenduru, TGS
Recently, Artificial Intelligence (AI) has been a prominent term used in the market. However, the mere mention of this technological innovation does not necessarily imply imminent change or increased efficiency across all industries. In the renewable energy sector, specifically Asset Performance Management (APM) providers, AI is frequently discussed, yet vendees often have differing interpretations of what constitutes AI implementation. The technical definition used by renewable energy industry professionals is as follows:
“Application of computational algorithms and machine learning techniques to analyze, predict, optimize, and automate processes in the generation, distribution, and consumption of renewable energy resources, with the goal of improving efficiency, reliability, and sustainability.”
This suggests that any digital tool enhanced by logical frameworks or supported by extensive datasets can be perceived as an application of AI. In this context, it is reasonable to assert that modern computing systems themselves are a product of AI principles. Yet, it is important to distinguish between general computational capabilities and true AI-driven functionalities, such as machine learning, natural language processing, or adaptive automation, which represent more advanced applications of artificial intelligence.
There are various methods and techniques employed to develop and train AI models for a wide range of applications. For APM providers keen on predictive data analytics, automation, control systems, forecasting, energy management, and other areas, the following AI modeling approaches are commonly used:
- First Principles Model (FPM): Derives system behavior from fundamental scientific laws and domain-specific principles, such as physics, chemistry and photovoltaics.
- Machine Learning (ML): Subset of AI that enables systems to learn patterns and make predictions or decisions by leveraging data without explicit programming. Within ML, there are several learning paradigms such as:
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- Supervised Learning – Models are trained on specified data to map inputs to known outputs.
- Unsupervised Learning – Identifies patterns or structures in unlabeled data without predefined outputs.
- Reinforced Learning – Models learn through a feedback system, using rewards and penalties to improve decisions based on actions taken.
The choice of AI modeling technique depends on the specific problem being addressed. For example, TGS’ asset management solution, powered by Prediktor, predominantly utilizes FPM (or a slight variation) for computing contractual or traceable metrics, such as Performance Ratio, Time-based Availability, Capacity Factor and Production Guarantee. These metrics are heavily reliant on physical measurements and empirical formulas and serve as a framework to ensure that assets perform as expected, meet contractual obligations, and generate long-term financial returns.
In contrast, Machine Learning techniques are more commonly used for forecasting, anomaly detection, predictive analytics, and grid optimization. These models require substantial amounts of data to identify patterns and relationships between various parameters. The accuracy of the model’s output depends on the quality of the input data and the geographic constraints of the asset. At TGS, Supervised Learning is applied to historical data for energy forecasting and predictive analytics. This method is particularly effective as it can handle large, complex datasets and provides the scalability needed by clients. It also minimizes the gap between predicted and actual values, ensuring accurate forecasts and predictions of future events.
As asset owners expand and diversify their portfolios, it is crucial to adopt a unified software platform that manages data across various asset types. Prediktor PowerView™ software offers a scalable and adaptable solution for Solar PV, Wind (onshore/offshore), and Energy Storage, designed to support both brownfield and greenfield assets. While the specifics of the AI models are hidden, the models are rigorously tested with historical data before applying to live data for operational usage.
Discover how AI applications can help you maximize asset performance and visit: https://www.tgs.com/solar