{"id":"W4416113997","doi":"10.1109/tpwrd.2025.3631805","title":"Optimization-Based Method for Aggregate Wind and Solar Capacity Estimation and Feeder Power Prediction","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aggregate (composite); Wind power; Artificial neural network; Grid; Grid connection; Power (physics); Photovoltaic system; Nameplate capacity; Distributed generation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004892386,0.0009844862,0.0008310463,0.0004638583,0.0002376448,0.0004927999,0.0007733668,0.0006127902,0.002701604],"category_scores_gemma":[0.001051207,0.000484838,0.0005823245,0.0005541917,0.0002837651,0.0005427581,0.0005587765,0.0009939185,0.0005689901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005963447,"about_ca_system_score_gemma":0.001382995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401983,"about_ca_topic_score_gemma":0.01122558,"domain_scores_codex":[0.9997873,0.00004302221,0.00001495999,0.00006358283,0.0000635387,0.00002764074],"domain_scores_gemma":[0.9997317,0.0001277195,0.00003288105,0.00001694732,0.00007730995,0.00001336171],"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":[0.00004559995,0.00002398159,0.0003563115,0.00003718106,0.00002789448,0.00003275974,0.00001402858,0.9297355,0.001387412,0.001733031,0.001159778,0.06544662],"study_design_scores_gemma":[0.000001308725,0.00000301696,0.00003806976,0.000001305235,0.000001464305,0.000002299005,8.567976e-7,0.9993747,0.0001428683,0.0002856071,0.0001472698,0.000001369302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006073124,0.0002584847,0.9911991,0.00007430847,0.00003439876,0.00003234545,0.0001012918,0.0006013602,0.001625568],"genre_scores_gemma":[0.5479229,0.0005730707,0.4426816,0.0001789676,0.0001228595,0.0003499174,0.0007675792,0.0002457892,0.007157221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01401983,"threshold_uncertainty_score":0.0278765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009137289572764712,"score_gpt":0.2324881616289472,"score_spread":0.2233508720561825,"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."}}