Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development
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 trie...
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Veröffentlicht in: | Sustainability 2017, Vol.9 (2), p.193-193 |
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Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 2071-1050 2071-1050 |
DOI: | 10.3390/su9020193 |