  With the rising costs of conventional sources of energy, the world is moving
towards sustainable energy sources including wind energy. Wind turbines consist
of several electrical and mechanical components and experience an enormous
amount of irregular loads, making their operational behaviour at times
inconsistent. Operations and Maintenance (O&M) is a key factor in monitoring
such inconsistent behaviour of the turbines in order to predict and prevent any
incipient faults which may occur in the near future. Machine learning has been
applied to the domain of wind energy over the last decade for analysing,
diagnosing and predicting wind turbine faults. In particular, we follow the
idea of modelling a turbine's performance as a power curve where any power
outputs that fall off the curve can be seen as performance errors. Existing
work using this idea has used data from a turbine's Supervisory Control &
Acquisition (SCADA) system to filter and analyse fault & alarm data using
regression techniques. In contrast to previous work, we explore how deep
learning can be applied to fault prediction from open access meteorological
data only.
