{"id":"W4382239995","doi":"10.1609/aaai.v37i4.25636","title":"MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring Based on a Dual-CNN Model","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"123 Certification (Canada)","funders":"National Natural Science Foundation of China","keywords":"Conditional random field; Computer science; Dual (grammatical number); State (computer science); Generalization; Power consumption; Artificial intelligence; Power (physics); Machine learning; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005250883,0.000308293,0.0002809087,0.0003620831,0.0001027302,0.000115384,0.000489746,0.00008974801,0.00003383602],"category_scores_gemma":[0.0002900191,0.0002844095,0.00009683375,0.0007036959,0.0001037518,0.0002252899,0.0001488331,0.0003807276,0.0002801624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002581406,"about_ca_system_score_gemma":0.00003618809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003642327,"about_ca_topic_score_gemma":0.00001917412,"domain_scores_codex":[0.997931,0.00001002356,0.0005803355,0.0004174612,0.0005534653,0.000507709],"domain_scores_gemma":[0.999191,0.00009568247,0.0001506918,0.0002416361,0.0002420921,0.00007887289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000772525,0.00007881216,0.001508772,0.0001293407,0.00001754927,0.000003805294,0.001778798,0.9322705,0.05438276,0.00507923,0.00008944751,0.004583705],"study_design_scores_gemma":[0.00006329198,0.00004122159,0.001262303,0.0005681738,0.000006857259,2.634836e-7,0.0006160596,0.726775,0.2687581,0.001692693,0.000006500406,0.0002095192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850658,0.000007139578,0.008793885,0.0002746744,0.0008111778,0.0004927609,0.000008023523,0.0003501883,0.004196391],"genre_scores_gemma":[0.9986599,0.00007864731,0.0008011887,0.00004640195,0.00005593302,0.0001362962,0.000001143036,0.00005028364,0.0001701842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2143753,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08998531909022335,"score_gpt":0.2933630415716923,"score_spread":0.203377722481469,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}