Artículo
Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach
Autor/es | Peña Muñoz, Manuel
Biscarri Triviño, Félix Personal Vázquez, Enrique León de Mora, Carlos |
Departamento | Universidad de Sevilla. Departamento de Tecnología Electrónica |
Fecha de publicación | 2022-02 |
Fecha de depósito | 2022-03-02 |
Publicado en |
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Resumen | In this paper, an intelligent data analysis method for modeling and optimizing energy
efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal
is to provide a Decision Support ... In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings. |
Identificador del proyecto | FP7- 285229 KnoHolEM |
Cita | Peña Muñoz, M., Biscarri Triviño, F., Personal Vázquez, E. y León de Mora, C. (2022). Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach. Sensors, 22 (4), 1380-. |
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