Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/37091
Title: Temperature control by its forecasting applying score fusion for sustainable development
Authors: Hernández-Travieso, José Gustavo
Herrera-Jiménez, Antonio L.
Travieso-González, Carlos M. 
Morgado-Dias, F.
Alonso-Hernández, Jesús B. 
Ravelo-García, Antonio G. 
UNESCO Clasification: 3308 Ingeniería y tecnología del medio ambiente
Keywords: Temperature forecasting
Sustainable development
Artificial neural network
Score fusion
Prediction system
Issue Date: 2017
Journal: Sustainability (Switzerland) 
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 degrees C for short period of time prediction and 5 degrees C for large period of time prediction.
URI: http://hdl.handle.net/10553/37091
ISSN: 2071-1050
DOI: 10.3390/su9020193
Source: Sustainability (Switzerland) [ISSN 2071-1050],v. 9 (2), 193
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