{"id":"W2889445225","doi":"10.1109/itec.2018.8450195","title":"Modeling EV fleet Load in Distribution Grids: A Data-Driven Approach","year":2018,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Power (physics); Electric vehicle; Work (physics); Automotive engineering; Vehicle dynamics; Port (circuit theory); Stochastic modelling; Simulation; Real-time computing; Electrical engineering; Engineering; Statistics; Mathematics","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":[0.0007212596,0.0006951533,0.0005521975,0.0007002008,0.000326321,0.000963276,0.001394931,0.0008782299,0.001163648],"category_scores_gemma":[0.002013015,0.0006068132,0.0007995097,0.000717189,0.0004028433,0.001365367,0.000551911,0.0008596932,0.0002450281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001137939,"about_ca_system_score_gemma":0.0008165665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01607749,"about_ca_topic_score_gemma":0.01329757,"domain_scores_codex":[0.9997193,0.00008974373,0.00001858467,0.0000747649,0.00006462253,0.0000330058],"domain_scores_gemma":[0.9993507,0.0003733422,0.00009125531,0.00005196814,0.0000994022,0.00003334348],"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":[0.00000600468,0.000009088664,0.0007204349,0.000007665935,0.000007374508,0.00002761802,0.00001193488,0.994644,0.0001595029,0.002212635,0.0001374449,0.002056276],"study_design_scores_gemma":[6.024172e-7,0.000001955385,0.00007411971,9.170168e-7,0.000001199298,0.000003480279,0.000004245714,0.9990045,0.00004270552,0.0007540147,0.00011086,0.000001375579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09283269,0.0001548992,0.9003899,0.0005317995,0.00004930446,0.00009053122,0.001152284,0.0004275991,0.004370962],"genre_scores_gemma":[0.9415726,0.0002598461,0.05357362,0.00006811917,0.00004295001,0.0001678672,0.0008611533,0.00009324907,0.003360573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01607749,"threshold_uncertainty_score":0.03196782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767705055282777,"score_gpt":0.2258037064723814,"score_spread":0.2081266559195536,"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."}}