{"id":"W4310584124","doi":"10.1109/smartgridcomm52983.2022.9961033","title":"A Neural Combinatorial Optimization Algorithm for Unit Commitment in AC Power Systems","year":2022,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Power system simulation; Solver; Computer science; Artificial neural network; Mathematical optimization; Algorithm; Electric power system; Optimization problem; Transformer; Generator (circuit theory); Power (physics); Mathematics; Artificial intelligence; Engineering; Voltage","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":[0.0001626855,0.0001015015,0.0001243332,0.00007429985,0.00007408287,0.00003474999,0.0001059012,0.00003503752,0.0001376225],"category_scores_gemma":[0.000007015406,0.0001166375,0.00003413902,0.0002107905,0.000006649228,0.00009892246,0.00004805923,0.0001204979,0.000003815367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002989858,"about_ca_system_score_gemma":0.00001065037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003435752,"about_ca_topic_score_gemma":0.000001307969,"domain_scores_codex":[0.999302,0.00003506095,0.0001971492,0.0001141847,0.0001515931,0.000199974],"domain_scores_gemma":[0.999761,0.00003260392,0.00001885352,0.0001190639,0.00003152963,0.00003690845],"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":[0.00001000685,0.00005419573,0.00005625749,0.00001116466,0.00001114018,0.0000022975,0.00004219071,0.9945413,0.00002376899,0.002226782,0.002708372,0.0003125505],"study_design_scores_gemma":[0.001123427,0.0001583899,0.0000589689,0.00000280813,0.000005598888,0.00000350015,0.0002500108,0.9904132,0.00004393308,0.00002680701,0.00778465,0.0001287415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02284461,0.0001977704,0.9630054,0.0001021545,0.008429768,0.001623203,0.0004860838,0.0005368816,0.002774155],"genre_scores_gemma":[0.9953794,0.000001860079,0.003605357,0.00001793982,0.00003626432,0.00039533,0.0004593703,0.00002734746,0.00007709284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9725348,"threshold_uncertainty_score":0.4756341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105216235491084,"score_gpt":0.2218436148943207,"score_spread":0.2113219913452123,"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."}}