{"id":"W2791314885","doi":"10.1109/tii.2018.2806936","title":"Unsupervised Nonintrusive Extraction of Electrical Vehicle Charging Load Patterns","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Smart meter; Smart grid; Computer science; Reliability (semiconductor); Metre; Electricity meter; Grid; Real-time computing; Electric vehicle; Interference (communication); Electrical load; Sampling (signal processing); Power (physics); Automotive engineering; Engineering; Electrical engineering; Voltage; Telecommunications","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":[0.0002137764,0.0006257697,0.0005364788,0.001261823,0.0002272043,0.0004726023,0.0005603937,0.0003235938,0.0006832051],"category_scores_gemma":[0.00130525,0.0002144618,0.000387367,0.001165899,0.0002287382,0.0006820324,0.0005787332,0.0004226106,0.0007133292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001695628,"about_ca_system_score_gemma":0.0004478648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340034,"about_ca_topic_score_gemma":0.003072165,"domain_scores_codex":[0.9996712,0.00005102792,0.00002380903,0.00009453409,0.0001101991,0.00004911582],"domain_scores_gemma":[0.9995133,0.0001777451,0.00007819498,0.00009595517,0.0001173534,0.00001751792],"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":[0.0004467972,0.0004627534,0.01938308,0.0001578548,0.0001348022,0.0005082512,0.0002922351,0.09914667,0.06349597,0.003219586,0.004711634,0.8080403],"study_design_scores_gemma":[0.00001975635,0.00008147191,0.01375071,0.0000148174,0.00003216988,0.0003467908,0.0001456175,0.9521128,0.02388368,0.004807772,0.004779458,0.00002505352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2044401,0.0002541109,0.7892513,0.0001396102,0.00004996941,0.0001044525,0.0009090035,0.001891908,0.002959571],"genre_scores_gemma":[0.8154204,0.0002368519,0.1758067,0.00007057023,0.00008815787,0.0001502351,0.00348869,0.000195321,0.004543001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001340034,"threshold_uncertainty_score":0.002664447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02505287703280818,"score_gpt":0.2333379390169957,"score_spread":0.2082850619841875,"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."}}