Temperature control by its forecasting applying score fusion for sustainable development

Abstract

Temperature control and its prediction has turned into a research challenge for the knowledge of the planet and its effects on different human activities and this will assure, in conjunction with energy efficiency, a sustainable development reducing CO2 emissions and fuel consumption. This work tries to offer a practical solution to temperature forecast and control, which has been traditionally carried out by specialized institutes. For the accomplishment of temperature estimation, a score fusion block based on Artificial Neural Networks was used. The dataset is composed by data from a meteorological station, using 20,000 temperature values and 10,000 samples of several meteorological parameters. Thus, the complexity of the traditional forecasting models is resolved. As a result, a practical system has been obtained, reaching a mean squared error of 0.136 °C for short period of time prediction and 5 °C for large period of time prediction.