{"id":"W4366779614","doi":"10.1016/j.egyai.2023.100267","title":"A data-driven framework for medium-term electric vehicle charging demand forecasting","year":2023,"lang":"en","type":"article","venue":"Energy and AI","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Demand forecasting; Term (time); Software deployment; Exploit; Electricity; Mean absolute percentage error; Peak demand; Mean squared error; Baseline (sea); Work (physics); Operations research; Artificial neural network; Artificial intelligence; Engineering; Statistics","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":[0.0009828494,0.0007944427,0.0008314584,0.0007994747,0.0003865682,0.001205835,0.002109697,0.001067694,0.002111132],"category_scores_gemma":[0.002384268,0.0005905902,0.0009141624,0.001119976,0.0003840396,0.001055062,0.0008818146,0.001854605,0.000603092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159043,"about_ca_system_score_gemma":0.001583301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03391821,"about_ca_topic_score_gemma":0.03816573,"domain_scores_codex":[0.9997208,0.00005712796,0.00002037356,0.00008697144,0.00006902872,0.00004577185],"domain_scores_gemma":[0.9992192,0.0004147213,0.00006028277,0.00005198593,0.0001973853,0.00005642051],"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":[0.00005374957,0.00005917705,0.001192614,0.00003696907,0.00004480054,0.00004923277,0.00001949962,0.9652024,0.0004255862,0.00303646,0.002440864,0.02743874],"study_design_scores_gemma":[0.000002285321,0.00000312445,0.00005493538,0.000001596601,0.000001262902,0.000002057825,0.000001708879,0.9986601,0.00004385741,0.001019532,0.000207992,0.000001661628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03371145,0.0007276617,0.9564564,0.0008419356,0.0002056853,0.00008540772,0.002745967,0.002549915,0.002675546],"genre_scores_gemma":[0.7328244,0.0005851118,0.2538485,0.0003699952,0.0002700801,0.0003520573,0.0070703,0.0002329748,0.00444656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03391821,"threshold_uncertainty_score":0.06744152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018352677623605,"score_gpt":0.2383109921505491,"score_spread":0.2199583145269441,"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."}}