Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances
Abstract Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-12-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-022-00242-3 |
| _version_ | 1797977229193379840 |
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| author | Marco Castangia Angelica Urbanelli Awet Abraha Girmay Christian Camarda Enrico Macii Edoardo Patti |
| author_facet | Marco Castangia Angelica Urbanelli Awet Abraha Girmay Christian Camarda Enrico Macii Edoardo Patti |
| author_sort | Marco Castangia |
| collection | DOAJ |
| description | Abstract Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world. |
| first_indexed | 2024-04-11T05:04:44Z |
| format | Article |
| id | doaj.art-eac57857399146e99b0696e58419d30e |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-04-11T05:04:44Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-eac57857399146e99b0696e58419d30e2022-12-25T12:31:28ZengSpringerOpenEnergy Informatics2520-89422022-12-015S411610.1186/s42162-022-00242-3Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliancesMarco Castangia0Angelica Urbanelli1Awet Abraha Girmay2Christian Camarda3Enrico Macii4Edoardo Patti5Department of Control and Computer Engineering (DAUIN), Politecnico di TorinoDepartment of Control and Computer Engineering (DAUIN), Politecnico di TorinoDepartment of Control and Computer Engineering (DAUIN), Politecnico di TorinoMidori s.r.l.Department of Control and Computer Engineering (DAUIN), Politecnico di TorinoDepartment of Control and Computer Engineering (DAUIN), Politecnico di TorinoAbstract Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.https://doi.org/10.1186/s42162-022-00242-3Smart gridsAppliance load monitoringOnline clusteringON/OFF devices |
| spellingShingle | Marco Castangia Angelica Urbanelli Awet Abraha Girmay Christian Camarda Enrico Macii Edoardo Patti Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances Energy Informatics Smart grids Appliance load monitoring Online clustering ON/OFF devices |
| title | Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances |
| title_full | Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances |
| title_fullStr | Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances |
| title_full_unstemmed | Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances |
| title_short | Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances |
| title_sort | non intrusive load monitoring techniques for the disaggregation of on off appliances |
| topic | Smart grids Appliance load monitoring Online clustering ON/OFF devices |
| url | https://doi.org/10.1186/s42162-022-00242-3 |
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