{"id":"W2625447160","doi":"10.1109/iciafs.2016.7946569","title":"Robust Non-Intrusive Load Monitoring (NILM) with unknown loads","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Subspace topology; Computer science; Energy (signal processing); Set (abstract data type); Maximum a posteriori estimation; A priori and a posteriori; Power (physics); Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Data mining; Mathematics; Statistics; Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777564,0.001022395,0.0007397773,0.0005850574,0.0002290843,0.0004818708,0.0009754137,0.0004295785,0.0005299788],"category_scores_gemma":[0.001740342,0.0002551195,0.0004403126,0.0006564426,0.000251595,0.0006736289,0.0007755909,0.0006802525,0.0003789725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205342,"about_ca_system_score_gemma":0.0002949593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001095113,"about_ca_topic_score_gemma":0.002219308,"domain_scores_codex":[0.9994797,0.0001087816,0.00002917068,0.0001840538,0.0001497546,0.00004848184],"domain_scores_gemma":[0.9995618,0.0001444588,0.0001026767,0.00009815193,0.00007596537,0.000016818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005100648,0.0002126027,0.01043997,0.0003594313,0.0001831335,0.0004217651,0.0003562015,0.1963994,0.0518957,0.003001057,0.004650477,0.7315702],"study_design_scores_gemma":[0.00001453257,0.00009038605,0.005092912,0.00001590071,0.00002082782,0.0001647313,0.00005692808,0.9772654,0.0131127,0.00194808,0.00219515,0.00002239402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08421446,0.0004876929,0.9109387,0.0001122747,0.00005678446,0.0000818286,0.0002692885,0.002113,0.001725832],"genre_scores_gemma":[0.7839788,0.0002284819,0.2121033,0.0001139597,0.0000679583,0.0001205844,0.0007894447,0.0001311313,0.002466351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001095113,"threshold_uncertainty_score":0.0025267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013792210629287,"score_gpt":0.172606104515082,"score_spread":0.1624681824087891,"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."}}