{"id":"W4387006109","doi":"10.1109/pesgm52003.2023.10252675","title":"Supervised Federated Neural Architecture Search and Its Application in Power System Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hitachi (Canada); Alberta Energy","funders":"","keywords":"Mean squared error; Computer science; Convergence (economics); Architecture; Baseline (sea); Artificial neural network; Machine learning; Artificial intelligence; Power (physics); Data mining; Time series; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001632329,0.0005306375,0.000718025,0.0006245555,0.0003040382,0.0005483653,0.0008064701,0.000876028,0.0008805337],"category_scores_gemma":[0.004389923,0.0002587805,0.0004241279,0.0005953486,0.0004047191,0.0008185854,0.0006075502,0.0006969068,0.0001504704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004521404,"about_ca_system_score_gemma":0.0008015037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004514762,"about_ca_topic_score_gemma":0.004467652,"domain_scores_codex":[0.9994728,0.0002489604,0.00002990727,0.0000770636,0.0001243618,0.00004690737],"domain_scores_gemma":[0.9987601,0.0006586151,0.0001139233,0.0001336267,0.0002910658,0.00004257325],"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":[0.0001087285,0.0001006104,0.001408271,0.00004998999,0.00008061773,0.00005734437,0.000052399,0.8419197,0.001520964,0.003202876,0.0008222435,0.1506764],"study_design_scores_gemma":[0.000003787134,0.00002171482,0.0001176003,0.000002567878,0.000004481398,0.000008806442,0.000003052942,0.9987354,0.000265202,0.0007248149,0.0001109715,0.000001703456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09818855,0.001333803,0.8961906,0.000249423,0.00009617489,0.00006129612,0.00005328572,0.0009589711,0.002867936],"genre_scores_gemma":[0.8821341,0.0002623989,0.1151002,0.00008786043,0.00004760526,0.00007015032,0.00008829377,0.00003747542,0.002171913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004514762,"threshold_uncertainty_score":0.008976936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210094892673765,"score_gpt":0.2181987284756601,"score_spread":0.1971892392082836,"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."}}