{"id":"W2750941888","doi":"10.1109/tsg.2017.2743760","title":"Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Computer science; A priori and a posteriori; Identification (biology); Load management; Data mining; Fuzzy logic; Demand response; Set (abstract data type); AC power; Smart meter; Smart grid; Real-time computing; Engineering; Artificial intelligence; Electricity; Voltage","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":[0.0002510309,0.0002930954,0.0002560065,0.00008300444,0.0007893718,0.0002259363,0.0002768312,0.00009468222,0.00001036332],"category_scores_gemma":[0.00001574883,0.0003275489,0.00009550016,0.00006473566,0.0000566096,0.000409716,0.0000072642,0.0002508494,0.00001877029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002806436,"about_ca_system_score_gemma":0.00002816794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001700061,"about_ca_topic_score_gemma":0.0002669041,"domain_scores_codex":[0.9986577,0.000009624037,0.0002927286,0.0003688329,0.0002709056,0.0004001934],"domain_scores_gemma":[0.9990195,0.0001053461,0.0001072764,0.0005607614,0.0000872405,0.0001198918],"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":[0.00009917075,0.00008810602,0.00677631,0.0007212743,0.0003683357,0.00004744608,0.0007342038,0.8605484,0.008734602,0.00003035877,0.0007503752,0.1211014],"study_design_scores_gemma":[0.003533359,0.0002843255,0.01422215,0.001019547,0.0002437819,0.00003454136,0.0004276529,0.7289091,0.2411031,0.0001740316,0.008456794,0.00159161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2702303,0.00002687819,0.7221944,0.00006608622,0.005328298,0.0004608167,0.00006429515,0.0002320774,0.001396811],"genre_scores_gemma":[0.9936321,0.00006270212,0.004681838,0.00001923894,0.0008806676,0.0004066271,0.000002245391,0.00007663431,0.0002379845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7234018,"threshold_uncertainty_score":0.9999177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02814137677123594,"score_gpt":0.2429811180423629,"score_spread":0.214839741271127,"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."}}