{"id":"W4409262385","doi":"10.1109/bdcat63179.2024.00016","title":"Deep Learning Models in Simulating and Analyzing Smart Grid Stability and Resilience","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Resilience (materials science); Computer science; Stability (learning theory); Grid; Deep learning; Smart grid; Artificial intelligence; Distributed computing; Machine learning; Geology; Engineering; Materials science; Electrical engineering","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":[0.0002784372,0.00009387569,0.0001041416,0.00008281994,0.0000489937,0.00008323636,0.00002907172,0.00003949541,0.00001818208],"category_scores_gemma":[0.00004192517,0.00008850796,0.00001338424,0.0001883939,0.00002530712,0.0002917011,0.00003852635,0.0002152327,8.061915e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002301774,"about_ca_system_score_gemma":0.000004130314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008144067,"about_ca_topic_score_gemma":0.0002678262,"domain_scores_codex":[0.9993938,0.00001982587,0.000158808,0.0001871612,0.00005790483,0.0001824935],"domain_scores_gemma":[0.9996676,0.0002134778,0.000005889076,0.00005879541,0.000006860608,0.00004736941],"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":[0.000001176096,0.000001247667,0.02505859,0.0001269008,0.000005256037,0.000007202958,0.001208485,0.94761,0.0009257288,0.0007675919,0.000001200589,0.02428669],"study_design_scores_gemma":[0.00005060187,0.000008391428,0.001435901,0.0001080087,0.000003362764,0.000003897117,0.0002253195,0.9970864,0.0004114086,0.0004657329,0.0000945566,0.0001063969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587635,0.003189862,0.02802564,0.00001046878,0.00009592339,0.00003244175,3.876085e-7,0.0002609774,0.009620751],"genre_scores_gemma":[0.9982162,0.0001279106,0.001568728,0.000004314062,0.00003263178,0.000002032739,0.000001300957,0.00001480722,0.00003208977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04947649,"threshold_uncertainty_score":0.360925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758767179918971,"score_gpt":0.2243625184708294,"score_spread":0.2067748466716397,"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."}}