{"id":"W4401243478","doi":"10.1016/j.apenergy.2024.124018","title":"Multi-time scales prediction of aggregated schedulable capacity of electric vehicle fleets based on enhanced Prophet-LGBM algorithm","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Project 211; Natural Science Foundation of Anhui Province; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Electric vehicle; Algorithm; Computer science; Automotive engineering; Engineering; Physics; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008019577,0.0002130796,0.0002817016,0.0002201818,0.00003580259,0.00001633718,0.0001336053,0.000192902,0.00005958307],"category_scores_gemma":[0.000006933069,0.0001976783,0.00006757453,0.0008755953,0.00003778232,0.00006584861,0.0000125649,0.0002040904,0.00001132661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009399276,"about_ca_system_score_gemma":0.00004020867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005211269,"about_ca_topic_score_gemma":0.000002809578,"domain_scores_codex":[0.9988549,0.00001686364,0.0003284109,0.0002621999,0.0002422914,0.0002953233],"domain_scores_gemma":[0.9995415,0.00004613577,0.00005312687,0.0002384454,0.00005562276,0.00006511893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001549325,0.00004889341,0.000002638383,0.0001151623,0.0000556696,0.000001168171,0.00002223246,0.1045947,0.8117121,0.0004559494,0.0001731573,0.08280288],"study_design_scores_gemma":[0.0002355267,0.00009035968,0.0001123886,0.00004188887,0.00001358409,7.540331e-7,0.000001600974,0.4939844,0.5051,0.000128498,0.0002075568,0.00008351052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8799251,0.0008620265,0.1136935,0.00001545073,0.0002408392,0.0002708143,0.00008587397,0.0007947184,0.004111699],"genre_scores_gemma":[0.9917745,0.00005902757,0.007849789,0.00001876083,0.00007935963,0.0000425947,0.00004496696,0.00005467892,0.00007634854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3893897,"threshold_uncertainty_score":0.8061085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004834470254709378,"score_gpt":0.1755299460827189,"score_spread":0.1706954758280095,"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."}}