Laurene Robinson

Class of 2026

Major: Computer Science

Mentor: Damiano Torre, PhD

University of Washington Tacoma

Optimizing Wind Turbine Management Through AI-Driven Load and

Placement Predictions

The maintenance and operation of wind turbines create challenges when it comes to maximizing

energy efficiency while also producing a minimal amount of wear. It is necessary to determine

when is the optimal time to stop a turbine, that being if it's due to mechanical issues, lack of

power demand, or environmental factors. Stopping the turbines can save companies resources

and improve their system's longevity. In this study, we explore the feasibility of using machine

learning to predict optimal loads for these conditions. By modeling variables such as wind

availability and current power demand, the model aims to predict what factors lead to the most

efficiency. Online simulated datasets might not involve environmental conditions, simulating the

data with perfect laminar flow. So we simulated our own data in order to see how turbulence

affects airflow patterns over time. To address ⁤⁤these challenges, this research proposes

developing an ⁤⁤AI model capable of continuously optimizing turbine ⁤⁤load and

placement decisions. Such a model would be able to integrate real-time data on wind conditions

and power demand, and then adapt accordingly to environmental changes along with operational

constraints. By using AI’s predictive capabilities, the study aims to increase energy production

efficiency by making the systems that control the turbines dynamic in the way they continuously

adapt to the conditions.

Keywords: machine learning , renewable energy , wind turbines , real time data

integration , power production