{"id":"W3212620435","doi":"10.1109/access.2021.3128491","title":"Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Duration (music); Performance prediction; Training set; Term (time); Work (physics); Artificial intelligence; Real-time computing; Machine learning; Simulation; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00006886566,0.00008479411,0.0001779328,0.0001605026,0.00004650765,0.0000453918,0.0001727743,0.00006841648,0.00006616757],"category_scores_gemma":[0.000006323784,0.00008561005,0.00008887372,0.0008113764,0.000008857673,0.0003134799,0.00001246633,0.00009353519,6.259333e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002574293,"about_ca_system_score_gemma":0.00001784289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006036737,"about_ca_topic_score_gemma":0.000006847454,"domain_scores_codex":[0.9993775,0.000005863479,0.0001934452,0.0001240619,0.000137305,0.0001618582],"domain_scores_gemma":[0.9996733,0.00002087243,0.00004105475,0.0001526196,0.00008094255,0.00003119507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001728691,0.00006053058,0.4553214,0.0003310578,0.001666393,0.000007195183,0.0006254561,0.3247252,0.09518377,0.00004311191,0.002687769,0.1193308],"study_design_scores_gemma":[0.0002464893,0.00002760515,0.5263618,0.00001534496,0.0009716483,0.00000418699,0.0000211832,0.2850864,0.1867642,0.00001049106,0.0003755398,0.0001151701],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993187,0.0001800933,0.005762434,0.000007035648,0.0003055079,0.00009548129,0.0001341971,0.00006773587,0.0002604808],"genre_scores_gemma":[0.9992947,0.00008157436,0.0002998578,0.00002173126,0.0001173575,0.00003991999,0.00009581976,0.00001507027,0.00003398079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1192157,"threshold_uncertainty_score":0.3491076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01807237462463733,"score_gpt":0.2573686841178203,"score_spread":0.239296309493183,"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."}}