Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development
AbstractTemperature 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. View Full-Text
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Hernández-Travieso, J.G.; Herrera-Jiménez, A.L.; Travieso-González, C.M.; Morgado-Dias, F.; Alonso-Hernández, J.B.; Ravelo-García, A.G. Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development. Sustainability 2017, 9, 193.
Hernández-Travieso JG, Herrera-Jiménez AL, Travieso-González CM, Morgado-Dias F, Alonso-Hernández JB, Ravelo-García AG. Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development. Sustainability. 2017; 9(2):193.Chicago/Turabian Style
Hernández-Travieso, José G.; Herrera-Jiménez, Antonio L.; Travieso-González, Carlos M.; Morgado-Dias, Fernando; Alonso-Hernández, Jesús B.; Ravelo-García, Antonio G. 2017. "Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development." Sustainability 9, no. 2: 193.
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