{"id":"W4402570352","doi":"10.1109/tia.2024.3462667","title":"Data-Driven Energy and Reserve Management of Prosumers Under Multi-Uncertainties","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Hong Kong Polytechnic University","keywords":"Energy management; Computer science; Energy (signal processing); Environmental economics; Economics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007008648,0.0001538365,0.0001147427,0.0001846483,0.00009078667,0.00004564947,0.0002836208,0.0001443025,0.00006128719],"category_scores_gemma":[2.071387e-7,0.000163879,0.00003418647,0.0004582359,0.00008502523,0.0001736508,0.000008510596,0.0002645089,0.00001091694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007380955,"about_ca_system_score_gemma":0.00001332854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006865526,"about_ca_topic_score_gemma":0.0001041732,"domain_scores_codex":[0.9991202,0.00001335375,0.0002293832,0.000318787,0.0001502079,0.0001681162],"domain_scores_gemma":[0.9991776,0.00003891091,0.00001675895,0.0006829205,0.00001968686,0.00006416505],"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":[0.000003195169,0.00007376797,0.000004723819,0.000261263,0.0004609665,0.000002712354,0.00003537455,0.9650354,0.0002560969,0.00838236,0.002802658,0.02268143],"study_design_scores_gemma":[0.0003809542,0.00002962751,0.0002774745,0.0002389183,0.0003426216,0.000007030185,0.0009819044,0.7898619,0.006183619,0.0003724409,0.200908,0.000415551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002085474,0.0002624047,0.9940946,0.0002676562,0.0003045962,0.0002870641,0.0003159216,0.0004270014,0.001955279],"genre_scores_gemma":[0.9911984,0.0005718914,0.004665418,0.00002585327,0.00004870761,0.0009284959,0.0000567163,0.00004927104,0.002455298],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9894292,"threshold_uncertainty_score":0.6682793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04499530492198332,"score_gpt":0.2756914651585082,"score_spread":0.2306961602365249,"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."}}