Predicting climate conditions using Internet-of-Things and artificial hydrocarbon networks

Hiram Ponce, Sebastián Gutiérrez, Alejandro Montoya

Abstract

The prediction and understanding of environmental conditions is of great importance to prevent and analyze changes in environment, supporting meteorological based sectors, such as agriculture. In that sense, this paper presents an Internet of Things (IoT) system for predicting climate conditions, 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 a field temperature sensor. 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. Preliminary results conclude that artificial hydrocarbon networks model predicts remote temperature with mean error of 0.05 °C in testing mode.

Keywords

artificial intelligence, DSA, EnOcean, Internet of Things, machine learning, predictive, Raspberry Pi, sensors, weather station, web service

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IMEKO-TC19-2017-013.pdf

DOI

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IMEKO TC

TC19 - Environmental Measurements

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