An indoor predicting climate conditions approach using Internet of Things and artificial hydrocarbon networks

An indoor predicting climate conditions approach using Internet of Things and artificial hydrocarbon networks 6000 4000 José David Castellanos

 

Autores: Hiram Eredín Ponce Espinosa y José Sebastián Gutiérrez Calderón

ABSTRACT. The prediction and understanding of environmental conditions are of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture or smart cities. In that sense, this paper presents an Internet of Things (IoT) system for predicting climate conditions inside enclosures, i.e. temperature, using artificial intelligence by means of a supervised learning method, the artificial hydrocarbon networks model. It allows predicting the temperature of remote locations using information from a web service comparing it with field temperature sensors. Experimental results of the supervised learning model are presented in two modes: offline training to detect the suitable parameters of the model and testing to validate the model with new data retrieval from the web service. Experimental results over ten days of data conclude that artificial hydrocarbon networks model helps to predict remote temperatures with root-mean square error of 2.7 °C in testing mode.

Ver en el repositorio institucional SCRIPTA:
http://scripta.up.edu.mx/xmlui/handle/123456789/4774

Autor UP: Hiram Ponce y José Sebastián Gutiérrez
Facultad de Ingeniería