An acquisition system of in-house parameters from wireless sensors for the identification of an environmental model
Giorgio Biagetti, Diego Coccia, Paolo Crippa, Laura Falaschetti, Claudio Turchetti · Procedia Computer Science · 2018
This paper presents a system for the acquisition of in-house parameters, such as temperature, pressure, humidity and so on, that can be used for the intelligent control of a building. The main objective of this work is to determine an environmental model of an in-house room using machine learning techniques. The system is based on a low data-rate network of sensing and control nodes to acquire the data, realized with a new protocol, called ToLHnet, that is able to employ both wired and wireless communication on different media. Several standard machine learning techniques, namely linear regression, classification and regression tree algorithm, support vector machine, have been used for the regression of the input-output thermal model. Additionally, a recently proposed new technique named particle-Bernstein polynomial has been successfully applied. Experimental results show that this technique outperforms the previous techniques, for both accuracy and computation time.