{"id":"W3008559474","doi":"10.1109/tie.2020.2973895","title":"Power Loss Prediction for Distributed Energy Resources: Rapid Loss Estimation Equation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Converters; Computer science; Microgrid; Reliability (semiconductor); Parametric statistics; Distributed generation; Power (physics); Battery (electricity); Electronic engineering; Control theory (sociology); Energy storage; Control engineering; Renewable energy; Engineering; Electrical engineering; Voltage; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000120828,0.0002290236,0.0002167,0.0001024981,0.0001879274,0.00007337674,0.000123087,0.0003345977,0.00009715914],"category_scores_gemma":[0.00002413576,0.0002546943,0.0001358462,0.0004495389,0.00002602526,0.0002471287,6.298067e-7,0.0003758312,0.00001010522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002915556,"about_ca_system_score_gemma":0.00007976067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006311684,"about_ca_topic_score_gemma":0.000007501307,"domain_scores_codex":[0.9987081,0.00004560631,0.000403137,0.0002680625,0.0002056701,0.0003694719],"domain_scores_gemma":[0.9994582,0.00009783939,0.00007392934,0.0001560431,0.00008904126,0.0001249667],"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":[0.0003674664,0.00004118257,9.620583e-7,0.00001146357,0.00009521224,5.674979e-7,0.000124727,0.9428069,0.001074555,0.0001316435,0.001203922,0.05414139],"study_design_scores_gemma":[0.002481257,0.0005647911,0.000001503101,0.00001812068,0.00009627456,0.000003286251,0.00001894763,0.9326349,0.02330172,0.0001099368,0.0405475,0.0002217196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003310094,0.0002512528,0.9935554,0.0006228198,0.0007062334,0.0004439828,0.000493023,0.0005612067,0.00005598929],"genre_scores_gemma":[0.9980599,0.0003758707,0.0004624786,0.0001260186,0.0003637153,0.0001777085,0.0003526614,0.00005884835,0.00002277481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9947498,"threshold_uncertainty_score":0.9999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748141982487729,"score_gpt":0.1975718145049442,"score_spread":0.180090394680067,"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."}}