Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach
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 effic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/4/1380 |
| _version_ | 1827652880862019584 |
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| author | Manuel Peña Félix Biscarri Enrique Personal Carlos León |
| author_facet | Manuel Peña Félix Biscarri Enrique Personal Carlos León |
| author_sort | Manuel Peña |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:07:16Z |
| format | Article |
| id | doaj.art-8a92c5071aa74501aa5bc8ac0070f332 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:07:16Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a92c5071aa74501aa5bc8ac0070f3322023-11-23T21:58:38ZengMDPI AGSensors1424-82202022-02-01224138010.3390/s22041380Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics ApproachManuel Peña0Félix Biscarri1Enrique Personal2Carlos León3Electronic Technology Department, School of Computer Science and Engineering, University of Seville, Av. Reina Mercedes S/N, 41012 Seville, SpainElectronic Technology Department, School of Computer Science and Engineering, University of Seville, Av. Reina Mercedes S/N, 41012 Seville, SpainElectronic Technology Department, School of Computer Science and Engineering, University of Seville, Av. Reina Mercedes S/N, 41012 Seville, SpainElectronic Technology Department, School of Computer Science and Engineering, University of Seville, Av. Reina Mercedes S/N, 41012 Seville, SpainIn 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.https://www.mdpi.com/1424-8220/22/4/1380smart buildingenergy efficiencydata analyticsenergy optimizationdecision support system |
| spellingShingle | Manuel Peña Félix Biscarri Enrique Personal Carlos León Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach Sensors smart building energy efficiency data analytics energy optimization decision support system |
| title | Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach |
| title_full | Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach |
| title_fullStr | Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach |
| title_full_unstemmed | Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach |
| title_short | Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach |
| title_sort | decision support system to classify and optimize the energy efficiency in smart buildings a data analytics approach |
| topic | smart building energy efficiency data analytics energy optimization decision support system |
| url | https://www.mdpi.com/1424-8220/22/4/1380 |
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