In the present time, we are facing a continuous growing of the energy price. It is then important to optimize the use of heat pumps, both in domestic and industrial environments. Using an opportunely labeled dataset of accelerometer, speed or relative position over time coming from a cheap sensor it is possible to estimate the I/O state of any heating or cooling engine. This new real-time measure allows then to compute the energy consumption and to study the most cheap usage scheme.
In this presentation we will show a real-case implementation of some fast binary classifiers, from basic statistics to machine learning, assessing the performance of each method in terms of computational time, precision and accuracy levels.
In the present time, we are facing a continuous growing of the energy price. It is then important to optimize the use of heat pumps, both in domestic and industrial environments. Using an opportunely labeled dataset of accelerometer, speed or relative position over time coming from a cheap sensor it is possible to estimate the I/O state of any heating or cooling engine. This new real-time measure allows then to compute the energy consumption and to study the most cheap usage scheme.
In this presentation we will show a real-case implementation of some fast binary classifiers, from basic statistics to machine learning, assessing the performance of each method in terms of computational time, precision and accuracy levels.
