{"id":"W3215884909","doi":"10.1109/ecce47101.2021.9595829","title":"Load Management Strategy for DC Fast Charging Stations","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Opal-Rt Technologies (Canada); McGill University","funders":"","keywords":"Voltage; Computer science; Energy management; Grid; Electrical engineering; Load balancing (electrical power); Electric vehicle; Load management; Base station; Charging station; Scheduling (production processes); Automotive engineering; Power (physics); Energy (signal processing); Engineering; Telecommunications; 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":[0.0002256837,0.0004327546,0.0003966644,0.0004287691,0.0006590953,0.001189874,0.001150083,0.0003506576,0.003788562],"category_scores_gemma":[0.0003308276,0.0001488955,0.0002403801,0.0003969341,0.0002405091,0.000555931,0.0004356663,0.0003906849,0.0008917851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007799014,"about_ca_system_score_gemma":0.0006282685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005170512,"about_ca_topic_score_gemma":0.004851159,"domain_scores_codex":[0.9997285,0.00003941463,0.0000161011,0.00007165794,0.00009005878,0.0000541897],"domain_scores_gemma":[0.9997782,0.00002895304,0.00002976297,0.00004910052,0.00009319169,0.00002075865],"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":[0.0007202565,0.0005478229,0.004567734,0.000133553,0.0000605282,0.0003491129,0.0002363277,0.5269857,0.08040228,0.01170745,0.008500297,0.3657889],"study_design_scores_gemma":[0.00005083505,0.0001124186,0.001274584,0.000004900758,0.0000196375,0.00006026394,0.00005893702,0.9772314,0.01517734,0.002144748,0.003847756,0.00001718833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2719347,0.0001944319,0.6905748,0.0003418457,0.0001112968,0.0003605958,0.0002183261,0.005105224,0.03115867],"genre_scores_gemma":[0.9841567,0.00002777546,0.01257009,0.0000358839,0.00001551554,0.00003269778,0.00006754823,0.00004272989,0.003050994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005170512,"threshold_uncertainty_score":0.01267397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03382519689726855,"score_gpt":0.3071800094182611,"score_spread":0.2733548125209925,"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."}}