{"id":"W4404799568","doi":"10.1016/j.apenergy.2024.124951","title":"Modeling and energy management of hangar thermo-electrical microgrid for electric plane charging considering multiple zones and resources","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microgrid; Electric potential energy; Energy management; Automotive engineering; Electric power; Energy (signal processing); Engineering; Environmental science; Electrical engineering; Power (physics); Renewable energy; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005493361,0.0001777087,0.0002059744,0.0001573176,0.00006251271,0.00004693108,0.00006592309,0.0000866273,0.000003485194],"category_scores_gemma":[0.00000137104,0.0001666359,0.0000322839,0.0002137557,0.00001721227,0.00003746674,0.00003286178,0.00008591521,1.73873e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000234204,"about_ca_system_score_gemma":0.00000494639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008950919,"about_ca_topic_score_gemma":0.00001650709,"domain_scores_codex":[0.9991634,0.000005108751,0.0001992053,0.0002496105,0.00008416989,0.000298459],"domain_scores_gemma":[0.9997463,0.00008074095,0.00001580281,0.00009410823,0.00001016528,0.00005294579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008002873,0.00001461362,0.00002702273,0.000737572,0.0003968677,0.0000153773,0.0004564697,0.0503426,0.1519219,0.08393717,0.0002688902,0.7118015],"study_design_scores_gemma":[0.000349418,0.00004542723,0.00002900369,0.00006983469,0.00004663962,0.00002251185,0.0000556186,0.9541928,0.03508308,0.001646387,0.008247793,0.0002115128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9018112,0.03097908,0.06585344,0.00002372465,0.00007929557,0.0000976382,0.00000707153,0.0002854023,0.0008631026],"genre_scores_gemma":[0.9933134,0.003698585,0.002704334,0.00003010954,0.0001037171,0.00005673577,0.0000114512,0.00005129636,0.00003035121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9038502,"threshold_uncertainty_score":0.6795214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0054139733806756,"score_gpt":0.1752845509460747,"score_spread":0.1698705775653991,"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."}}