{"id":"W3126866643","doi":"10.1109/epec48502.2020.9319916","title":"Ensemble Learning for Charging Load Forecasting of Electric Vehicle Charging Stations","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Dependency (UML); Ensemble learning; Electric power system; Electric vehicle; Term (time); Recurrent neural network; Artificial intelligence; Machine learning; Power (physics)","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":[0.001279933,0.0008529953,0.001058775,0.001337803,0.000332635,0.0005972796,0.0009529432,0.0006485785,0.000657513],"category_scores_gemma":[0.002708633,0.000272091,0.0006957402,0.001505902,0.0001236811,0.001260717,0.0005064486,0.00111141,0.0003564437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005396216,"about_ca_system_score_gemma":0.0004801501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008958167,"about_ca_topic_score_gemma":0.01021384,"domain_scores_codex":[0.9995983,0.00009079121,0.00003151067,0.0001177611,0.0001005426,0.00006113545],"domain_scores_gemma":[0.999143,0.000319286,0.0000729938,0.0001390949,0.000291171,0.00003432386],"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":[0.0001916323,0.0001920672,0.0121013,0.0000379146,0.0001617345,0.00008172076,0.00005580711,0.6999254,0.001740985,0.0007528652,0.003687742,0.2810709],"study_design_scores_gemma":[0.000001688544,0.00001062649,0.0007087233,0.000001945192,0.000007780485,0.000005727332,0.000006103525,0.9983909,0.0003505223,0.0003191126,0.0001936986,0.000003114143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4133679,0.002037382,0.5750201,0.0005286928,0.000267307,0.00007996871,0.001643063,0.002678765,0.004376875],"genre_scores_gemma":[0.9546821,0.0004654987,0.04080796,0.00006012663,0.00009341814,0.0000466477,0.002338632,0.0000421718,0.001463393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008958167,"threshold_uncertainty_score":0.01781201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382277137979625,"score_gpt":0.201203567251525,"score_spread":0.1873807958717287,"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."}}