  The prediction of electrical power in combined cycle power plants is a key
challenge in the electrical power and energy systems field. This power output
can vary depending on environmental variables, such as temperature, pressure,
and humidity. Thus, the business problem is how to predict the power output as
a function of these environmental conditions in order to maximize the profit.
The research community has solved this problem by applying machine learning
techniques and has managed to reduce the computational and time costs in
comparison with the traditional thermodynamical analysis. Until now, this
challenge has been tackled from a batch learning perspective in which data is
assumed to be at rest, and where models do not continuously integrate new
information into already constructed models. We present an approach closer to
the Big Data and Internet of Things paradigms in which data is arriving
continuously and where models learn incrementally, achieving significant
enhancements in terms of data processing (time, memory and computational
costs), and obtaining competitive performances. This work compares and examines
the hourly electrical power prediction of several streaming regressors, and
discusses about the best technique in terms of time processing and performance
to be applied on this streaming scenario.
