{"id":"W2889402862","doi":"10.1109/ccece.2018.8447822","title":"Power Appliance Disaggregation Framework Via Hybrid Hidden Markov Model","year":2018,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Hidden Markov model; Computer science; Energy consumption; Identification (biology); Consumption (sociology); Pareto principle; Power consumption; Markov chain; Internet of Things; Markov model; Machine learning; Power (physics); Distributed computing; Artificial intelligence; Computer security; Engineering","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":[0.0007419455,0.0005261755,0.0009217106,0.0005089411,0.0003976308,0.0009039557,0.001346549,0.0006754372,0.002631556],"category_scores_gemma":[0.001191158,0.0004589114,0.0008938079,0.000786415,0.0003319239,0.000943461,0.0007724524,0.000814721,0.0004886397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651505,"about_ca_system_score_gemma":0.0008756254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01924121,"about_ca_topic_score_gemma":0.01751163,"domain_scores_codex":[0.9995902,0.0001178417,0.00002399574,0.0001144472,0.00009464668,0.00005890324],"domain_scores_gemma":[0.9996107,0.0002344455,0.00004268712,0.00003273971,0.00006022662,0.00001923432],"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":[0.00004114327,0.00003363699,0.0009513009,0.00003243289,0.00004910854,0.00007072,0.00005469255,0.9605565,0.0006196134,0.01079671,0.0007747073,0.02601943],"study_design_scores_gemma":[0.000001029228,0.000002363051,0.0000457574,7.773188e-7,0.000002651921,0.000002933598,0.000001885403,0.9978566,0.0000378472,0.001920397,0.0001264049,0.000001404607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01244212,0.0003324646,0.9841951,0.000169507,0.00004028694,0.00002630377,0.0001832717,0.0007468992,0.001864033],"genre_scores_gemma":[0.8828903,0.0004002984,0.1103306,0.0001148592,0.00009615485,0.0001334996,0.000734503,0.0001012997,0.00519841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01924121,"threshold_uncertainty_score":0.03825843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006140883603115225,"score_gpt":0.2058305685494297,"score_spread":0.1996896849463144,"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."}}