Micro Gas Turbine Electrical Energy Prediction

In this project, I applied regression models to predict the electrical power output of a 3-kilowatt micro gas turbine based on its input control signal over time.
The dataset included over 71,000 time-series measurements, capturing both smooth changes and sudden transitions where the turbine’s output lagged behind the input.
Using regression allowed me to model not just the steady-state relationships, but also the more challenging transitional behaviors where delays made the prediction task less straightforward.
I was motivated by how regression models can go beyond simple curve fitting to capture real-world engineering systems. Since micro turbines play a role in distributed and sustainable energy, being able to accurately forecast their performance with data-driven methods has practical value for improving efficiency, reliability, and energy planning.