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Descripción
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| Hospitals are large buildings that consume a great amount of energy mostly due to their continuous energy consumption needs, high consumer medical equipment, and special requirements of thermal and air conditions. Reliable dynamic simulation is a chimera because of the complex design and behavior of these buildings. Therefore, monitoring-based methods arise as a plausible alternative. Its main drawback, however, is the lack of enough data to generate statistically robust models. The paper faces this problem thanks to the helpful contribution of a collaborative hospital which was able to generate daily data of electrical energy consumption for a period of six years. Besides, thirteen variables that summarize the daily activity of the hospital are also included. The results show how machine learning techniques generate models that accurately predict the electrical energy consumption based on weather conditions and activity measurements. The obtained results are useful for the design of more specific energy saving strategies, a more efficient economic investment for energy retrofitting of existing buildings and a better management of economic energy cost in large-scale buildings. | |
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Internacional
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Si |
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Nombre congreso
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22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2017) |
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Tipo de participación
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960 |
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Lugar del congreso
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Limassol, Cyprus |
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Revisores
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Si |
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ISBN o ISSN
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978-1-5090-6505-9 |
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DOI
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10.1109/ETFA.2017.8247667 |
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Fecha inicio congreso
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12/09/2017 |
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Fecha fin congreso
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15/09/2017 |
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Desde la página
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1 |
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Hasta la página
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7 |
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Título de las actas
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Conference Paper ETFA2017 |