Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters
The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/7/1034 |
| _version_ | 1797407154646286336 |
|---|---|
| author | Nicoleta Stroia Daniel Moga Dorin Petreus Alexandru Lodin Vlad Muresan Mirela Danubianu |
| author_facet | Nicoleta Stroia Daniel Moga Dorin Petreus Alexandru Lodin Vlad Muresan Mirela Danubianu |
| author_sort | Nicoleta Stroia |
| collection | DOAJ |
| description | The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction). |
| first_indexed | 2024-03-09T03:37:48Z |
| format | Article |
| id | doaj.art-bb95e9214a9848c0b9c54f8b4d743a15 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T03:37:48Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-bb95e9214a9848c0b9c54f8b4d743a152023-12-03T14:46:41ZengMDPI AGBuildings2075-53092022-07-01127103410.3390/buildings12071034Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential ClustersNicoleta Stroia0Daniel Moga1Dorin Petreus2Alexandru Lodin3Vlad Muresan4Mirela Danubianu5Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaAutomation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaApplied Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaBasis of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaAutomation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaFaculty of Electrical Engineering and Computer Science, “Stefan cel Mare” University of Suceava, 720229 Suceava, RomaniaThe monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction).https://www.mdpi.com/2075-5309/12/7/1034building-energy monitoringsmart-meter nodedistributed sensingcloud databaseload profile modeling |
| spellingShingle | Nicoleta Stroia Daniel Moga Dorin Petreus Alexandru Lodin Vlad Muresan Mirela Danubianu Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters Buildings building-energy monitoring smart-meter node distributed sensing cloud database load profile modeling |
| title | Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters |
| title_full | Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters |
| title_fullStr | Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters |
| title_full_unstemmed | Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters |
| title_short | Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters |
| title_sort | integrated smart home architecture for supporting monitoring and scheduling strategies in residential clusters |
| topic | building-energy monitoring smart-meter node distributed sensing cloud database load profile modeling |
| url | https://www.mdpi.com/2075-5309/12/7/1034 |
| work_keys_str_mv | AT nicoletastroia integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters AT danielmoga integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters AT dorinpetreus integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters AT alexandrulodin integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters AT vladmuresan integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters AT mireladanubianu integratedsmarthomearchitectureforsupportingmonitoringandschedulingstrategiesinresidentialclusters |