2022-09-01 –, HS 120
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.
IoT data can help you monitoring your heat pump (or any angine) and save energy!
Domains –Machine Learning, Statistics, Vector and array manipulation
Expected audience expertise: Domain –none
Expected audience expertise: Python –some
Data Analyst at FAR Networks srl.
Former Post-Doc fellow at the PNC - Padova Neuroscience Center, Italy.
PhD in Neurosciences, with a Master's degree in Mathematics.