  The increasing usage of smartphones in everyday tasks has been motivated many
studies on energy consumption characterization aiming to improve smartphone
devices' effectiveness and increase user usage time. In this scenario, it is
essential to study mechanisms capable of characterizing user usage patterns, so
smartphones' components can be adapted to promote the best user experience with
lower energy consumption. The goal of this study is to build an energy
consumption model based on user usage patterns aiming to provide the best
accurate model to be used by application developers and automated optimization.
To develop the energy consumption models, we established a method to identify
the components with the most influence in the smartphone's energy consumption
and identify the states of each influential device. Besides that, we
established a method to prove the robustness of the models constructed using
inaccurate hardware and a strategy to assess the accuracy of the model built.
After training and testing each strategy to model the energy consumption based
on the user's usage and perform the Nemenyi test, we demonstrated that it is
possible to get a Mean Absolute Error of 158.57mW when the smartphone's average
power is 1970.1mW. Some studies show that the leading smartphone's workload is
the user. Based on this fact, we developed an automatic model building
methodology that is capable of analyzing the user's usage data and build smart
models that can estimate the smartphone's energy consumption based on the
user's usage pattern. With the automatic model building methodology, we can
adopt strategies to minimize the usage of components that drain the battery.
