{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002075087,0.001345262,0.0007423024,0.0007360479,0.0003866144,0.0006243286,0.0008551779,0.001085191,0.001513634],"category_scores_gemma":[0.006031009,0.0002743654,0.0004589317,0.0006694781,0.0003016562,0.001552686,0.0005838169,0.001409339,0.0006186798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009781925,"about_ca_system_score_gemma":0.0009492257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413941,"about_ca_topic_score_gemma":0.01172339,"domain_scores_codex":[0.9993744,0.0001680396,0.00005899329,0.0001476675,0.0001366436,0.0001143043],"domain_scores_gemma":[0.9980573,0.001245301,0.0001124363,0.0001230274,0.0003917234,0.00007019637],"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":[0.001093269,0.000390041,0.01373001,0.0003371082,0.0003150557,0.0002247703,0.0001705495,0.6190612,0.01122241,0.001061276,0.004474233,0.34792],"study_design_scores_gemma":[0.000005680786,0.00008159805,0.001118908,0.00001055777,0.00001583781,0.00002333384,0.00002718626,0.9953832,0.002815007,0.000333419,0.0001769219,0.000008332991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8178334,0.005640777,0.1609843,0.001656465,0.0005803497,0.00008798613,0.001327942,0.005230799,0.006658056],"genre_scores_gemma":[0.9811507,0.0004514538,0.01613548,0.0001386223,0.00004379398,0.00003938341,0.0008893902,0.00005326329,0.001097943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01413941,"threshold_uncertainty_score":0.0281142,"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."}}