{"id":"W3160258797","doi":"10.1109/tsg.2021.3078695","title":"TraceGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bottleneck; Computer science; Generator (circuit theory); Smart meter; Discriminative model; Generative grammar; Generative adversarial network; Data mining; Adversarial system; Electricity; Conditional random field; Power (physics); Machine learning; Data collection; Artificial intelligence; Engineering; Deep learning; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001178315,0.000327899,0.00028661,0.0001285022,0.000255319,0.00008628705,0.0001612631,0.0001841324,0.0003553531],"category_scores_gemma":[0.000004115552,0.0003707314,0.000184026,0.0004502931,0.00004545389,0.0002203176,0.000002600329,0.0004970256,0.00004677198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143912,"about_ca_system_score_gemma":0.00003560992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001781206,"about_ca_topic_score_gemma":0.00008629009,"domain_scores_codex":[0.9984997,0.00006827584,0.0002988363,0.0004081733,0.0002760701,0.0004489695],"domain_scores_gemma":[0.999258,0.0001132167,0.00003431166,0.0004153979,0.00006147664,0.0001176281],"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":[0.00002460982,0.00005944771,0.000003707002,0.00001665306,0.0002162159,0.00006192474,0.0001058411,0.986164,0.0100338,0.00005967955,0.001592839,0.001661245],"study_design_scores_gemma":[0.0006076764,0.0000360667,0.00008433362,0.00009283693,0.0001434267,0.0000228206,0.0001712149,0.7960482,0.1781231,0.00001653643,0.02402421,0.0006295281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01933503,0.0004646513,0.9668553,0.00008392087,0.01044798,0.000184874,0.00002788131,0.0004574245,0.002142978],"genre_scores_gemma":[0.9923318,0.000145013,0.005927074,0.000235605,0.0008847378,0.00006201786,0.000007973422,0.00009710596,0.0003086948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9729968,"threshold_uncertainty_score":0.9998745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302066608257227,"score_gpt":0.2108780105502581,"score_spread":0.1978573444676859,"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."}}