Bouilloud, M., Nakhla, M. (2026). Integrated optimization and machine learning through hyperparameter selection: an application to predictive maintenance of wind turbines. Proceeding OLA, Optimization and Learning.
This paper examines how optimization and machine learning can be
combined to improve predictive maintenance in wind farms.
Optimization integrated with machine learning creates powerful meth-
ods to solve complex problems by improving solution search strate-
gies and enhancing decision-making processes, in contrast with the
usual sequential approach. This synergy enables algorithms to learn
from data and guide optimization procedures more effectively. In this
proposal, we present an application of this combination for predic-
tive maintenance, where a MIP schedules tasks to minimize downtime
while maximizing system reliability and machine learning models, in-
cluding random forrest and isolation forrest, anticipate equipment fail-
ures.
The model we propose mobilizes ensemble learning with a loop such
that the performance of the corresponding tasks planning allows to ad-
just the threshold for aggregating the results of the different prediction
models. The predictions are adjusted through iterations by the results
of the optimization. The optimization then leads to a return to the en-
semble learning, becoming similar to a layer of a learning algorithm.
Experimentations were carried out on industrial data.