IEDL: Indian Energy Dataset with Low frequency for NILM
Energy conservation has received a lot of attention in recent ten years. Adoption of sustainable energy is important for meeting energy demand, to address this Non-Intrusive Load Monitoring (NILM) technology is now being developed all over the world. Several energy consumption datasets have been rel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722009830 |
| _version_ | 1797947043991257088 |
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| author | Deepika R. Chavan Dagadu S. More Amruta M. Khot |
| author_facet | Deepika R. Chavan Dagadu S. More Amruta M. Khot |
| author_sort | Deepika R. Chavan |
| collection | DOAJ |
| description | Energy conservation has received a lot of attention in recent ten years. Adoption of sustainable energy is important for meeting energy demand, to address this Non-Intrusive Load Monitoring (NILM) technology is now being developed all over the world. Several energy consumption datasets have been released; each dataset has different properties, uses, and limitations. Thus, a solid comprehension of the relevant datasets will help to improve NILM system. This work is dedicated to detailed study of low frequency (data sampling rate lower than Alternating Current (AC) fundamental frequency) residential datasets, a total eighteen datasets are compared according to their measurement features, collected location, nature of sampling, data collection duration, data development platform etc. Furthermore, datasets are classified depending upon the appliance level and aggregated level data. To full fill the literature gap a new low-cost Indian Energy Dataset with Low frequency (IEDL) has been developed with detailed system deployment. This low frequency dataset collects data from aged appliances (older than ten years), which is allowed to adopt NILM applications in energy saving, recommendation system, appliance behavior, demand prediction area. |
| first_indexed | 2024-04-10T21:21:34Z |
| format | Article |
| id | doaj.art-4582cc5c71474ee6b9d431fa3f8700d3 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T21:21:34Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-4582cc5c71474ee6b9d431fa3f8700d32023-01-20T04:25:03ZengElsevierEnergy Reports2352-48472022-11-018701709IEDL: Indian Energy Dataset with Low frequency for NILMDeepika R. Chavan0Dagadu S. More1Amruta M. Khot2Corresponding author.; Walchand College of Engineering, Sangli, IndiaWalchand College of Engineering, Sangli, IndiaWalchand College of Engineering, Sangli, IndiaEnergy conservation has received a lot of attention in recent ten years. Adoption of sustainable energy is important for meeting energy demand, to address this Non-Intrusive Load Monitoring (NILM) technology is now being developed all over the world. Several energy consumption datasets have been released; each dataset has different properties, uses, and limitations. Thus, a solid comprehension of the relevant datasets will help to improve NILM system. This work is dedicated to detailed study of low frequency (data sampling rate lower than Alternating Current (AC) fundamental frequency) residential datasets, a total eighteen datasets are compared according to their measurement features, collected location, nature of sampling, data collection duration, data development platform etc. Furthermore, datasets are classified depending upon the appliance level and aggregated level data. To full fill the literature gap a new low-cost Indian Energy Dataset with Low frequency (IEDL) has been developed with detailed system deployment. This low frequency dataset collects data from aged appliances (older than ten years), which is allowed to adopt NILM applications in energy saving, recommendation system, appliance behavior, demand prediction area.http://www.sciencedirect.com/science/article/pii/S2352484722009830Low frequency NILMEnergy disaggregationEnergy datasetsDataset developmentSustainable energy |
| spellingShingle | Deepika R. Chavan Dagadu S. More Amruta M. Khot IEDL: Indian Energy Dataset with Low frequency for NILM Energy Reports Low frequency NILM Energy disaggregation Energy datasets Dataset development Sustainable energy |
| title | IEDL: Indian Energy Dataset with Low frequency for NILM |
| title_full | IEDL: Indian Energy Dataset with Low frequency for NILM |
| title_fullStr | IEDL: Indian Energy Dataset with Low frequency for NILM |
| title_full_unstemmed | IEDL: Indian Energy Dataset with Low frequency for NILM |
| title_short | IEDL: Indian Energy Dataset with Low frequency for NILM |
| title_sort | iedl indian energy dataset with low frequency for nilm |
| topic | Low frequency NILM Energy disaggregation Energy datasets Dataset development Sustainable energy |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722009830 |
| work_keys_str_mv | AT deepikarchavan iedlindianenergydatasetwithlowfrequencyfornilm AT dagadusmore iedlindianenergydatasetwithlowfrequencyfornilm AT amrutamkhot iedlindianenergydatasetwithlowfrequencyfornilm |