In aeolytics, several types of data analytics are employed to optimize wind energy operations. Descriptive analytics are used to understand historical data and operational patterns, helping to identify trends and anomalies. Diagnostic analytics delve deeper, analyzing why certain trends or issues arise, often through root cause analysis. Predictive analytics utilize statistical algorithms and machine learning techniques to forecast future energy outputs and maintenance needs, aiding in proactive decision-making. Prescriptive analytics suggest actions to optimize outcomes based on the predictive models. Each type of analytics plays a crucial role in maximizing wind farm efficiency and ensuring reliability. Combining these analytics provides a comprehensive approach to managing wind energy resources effectively.
Key Takeaway: Aeolytics uses descriptive, diagnostic, predictive, and prescriptive analytics to optimize wind energy operations.