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

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. © 2018 Elsevier Ltd