AI for Renewables: Use Cases Beyond Failure Prediction

The initial applications of AI platforms for renewable energy were mainly focused on failure prediction and anomaly detection. While these applications remain important to the industry, the scope of uses of AI in renewables has greatly increased. Early AI platforms primarily used SCADA data for predictions, while modern platforms can ingest and utilize SCADA, event, budget, maintenance, and CMS data. Time consuming tasks such as the creation of recurring reports or categorizing downtime can now be performed by AI platforms, resulting in significant time savings. The addition of natural language processing (NLP) functionality to AI platforms allows for the ingestion of unstructured data such as maintenance history and inspection reports. Forecasting tools can be used to develop optimal operations and maintenance strategies, and advanced AI enabled cybersecurity tools can protect equipment from malicious actors. During this webinar, renewable energy and AI experts from SparkCognition will share details on how renewable energy operators are using each of these expanded capabilities to improve the operation of their assets.