| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_53f347c8f604f15767e8e73fec62095a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H02J2203-20 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H02J3-003
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H02J13-00002 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2020-09-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c386b302f0f0e0a17135307540911185
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fb9bb2e656bca7f812abaf20ff46e87b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_755af630dd3405138c2030cebd9c562d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e7633e7e91e9fb990014d7f1fc60e384 |
| publicationDate |
2021-10-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113574550-A |
| titleOfInvention |
Non-Intrusive Load Monitoring Using Integrated Machine Learning Technology |
| abstract |
Embodiments enable non-intrusive load monitoring using integrated machine learning techniques. A first trained machine learning model configured to decompose target device energy usage from source location energy usage and a second trained machine configured to detect device energy usage from source location energy usage may be stored learning a model, wherein the first trained machine learning model is trained to predict energy usage of the target device and the second trained machine learning model is trained to predict when the target device has used energy. Source location energy usage may be received over a period of time, where the source location energy usage includes energy consumed by the target device. The disaggregated target device energy usage over a period of time may be predicted based on the received source location energy usage using the first and second trained machine learning models. |
| priorityDate |
2019-11-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |