{"id":"W6902976403","doi":"10.1016/j.jpowsour.2025.237882","title":"A hybrid deep learning model for load forecasting of electric vehicle charging stations using time series decomposition","year":2025,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council of Science and Technology; Chinese Academy of Sciences; Science, Technology and Innovation Commission of Shenzhen Municipality; Canadian Anesthesiologists' Society","keywords":"Robustness (evolution); Benchmark (surveying); Hilbert–Huang transform; Noise (video); Series (stratigraphy); Electric vehicle; Generalization; Time series","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000335267,0.0004598982,0.0006411092,0.0003350168,0.0002183195,0.000582365,0.0008232967,0.0006819502,0.001663626],"category_scores_gemma":[0.0006303777,0.0003628811,0.0004874376,0.0004540291,0.0002036902,0.0006785008,0.000408894,0.001064463,0.000378778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000628538,"about_ca_system_score_gemma":0.000743123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02327222,"about_ca_topic_score_gemma":0.0200677,"domain_scores_codex":[0.999903,0.00001684073,0.000006264435,0.00003035632,0.00002008843,0.00002342714],"domain_scores_gemma":[0.9998046,0.00007868687,0.00002044811,0.00001537355,0.0000640106,0.00001693321],"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":[0.00003812002,0.00004564197,0.0008136809,0.00001293528,0.00002614724,0.00002382661,0.000008231125,0.9723426,0.0007514328,0.001221647,0.0007037109,0.02401206],"study_design_scores_gemma":[5.186538e-7,0.00000127594,0.00003752227,4.286265e-7,8.673318e-7,6.408907e-7,3.375746e-7,0.9997632,0.0000339922,0.0001383858,0.00002238927,4.949728e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2127646,0.0007908921,0.7785559,0.000591785,0.0001964918,0.0000293747,0.0006049885,0.001090411,0.005375553],"genre_scores_gemma":[0.9734995,0.0002166017,0.02039867,0.00009032043,0.00004773904,0.00003339116,0.0004839812,0.00003383345,0.00519608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02327222,"threshold_uncertainty_score":0.04627353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006870213476454338,"score_gpt":0.2256905856363584,"score_spread":0.2188203721599041,"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."}}