{"id":"W4313562578","doi":"10.1109/epec56903.2022.10000164","title":"Optimized Hybrid Neural Network for Wind Speed Forecasting","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Wind speed; Convolutional neural network; Mean squared error; Artificial intelligence; Wind power; Support vector machine; Artificial neural network; Bayesian optimization; Feature extraction; Feature (linguistics); Deep learning; Pattern recognition (psychology); Random forest; Fuzzy logic; Machine learning; Mathematics; Engineering; Statistics","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":[0.0002197368,0.000145482,0.0001731328,0.00004243398,0.0003030999,0.00003424474,0.0001530115,0.00001714855,0.0005015108],"category_scores_gemma":[0.00002011823,0.0001523761,0.0001071578,0.000148817,0.000009595276,0.00007982287,0.00007865731,0.0001676444,0.000002661253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004432553,"about_ca_system_score_gemma":0.000008223805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009657311,"about_ca_topic_score_gemma":0.000002049441,"domain_scores_codex":[0.999055,0.00001787317,0.0002161022,0.0001549044,0.0001177777,0.0004383344],"domain_scores_gemma":[0.9996035,0.0001589809,0.00002849102,0.0001281331,0.00001485651,0.00006605287],"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":[0.00002357932,0.000004615459,0.00007262212,0.00001425288,0.00002483245,0.000008420911,0.00004632741,0.9801521,0.0001026539,0.0002269095,0.01493852,0.00438516],"study_design_scores_gemma":[0.0006123199,0.0000519161,0.000009172017,0.000005587636,0.00001235126,0.00005340476,0.00004529271,0.9653153,0.0003470662,0.0002348558,0.03311626,0.0001964544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8597027,0.000760128,0.04658154,0.0001679617,0.0086549,0.0007875674,0.00009566789,0.001890698,0.08135886],"genre_scores_gemma":[0.9733557,0.000002399485,0.02380188,0.0001712582,0.0006792725,0.00001975769,0.00008447105,0.00007247183,0.001812845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.113653,"threshold_uncertainty_score":0.6213716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462193454317747,"score_gpt":0.2106837153825046,"score_spread":0.1860617808393271,"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."}}