{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004195739,0.0005678372,0.0005205252,0.0003970876,0.0001594661,0.000502505,0.0005401333,0.0005935378,0.00126137],"category_scores_gemma":[0.0007408084,0.0002944598,0.0003337045,0.0004365116,0.0001664259,0.0006614228,0.0002862893,0.0005743541,0.0002668725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141663,"about_ca_system_score_gemma":0.0006193403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01380623,"about_ca_topic_score_gemma":0.01698849,"domain_scores_codex":[0.9998282,0.00003328861,0.00001311957,0.00004415638,0.00005198687,0.0000292296],"domain_scores_gemma":[0.9998092,0.00007556994,0.00002397588,0.00001069064,0.00007418812,0.000006461968],"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":[0.00008495891,0.0000443,0.000791349,0.00003713024,0.00005057473,0.00003384122,0.00001122926,0.9359022,0.00219532,0.001032898,0.0009418752,0.05887441],"study_design_scores_gemma":[0.000001352504,0.000006046665,0.0001078995,0.000001485696,0.000002625769,0.000002082075,7.325915e-7,0.9994175,0.000193327,0.000203084,0.00006242836,0.000001337952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.151422,0.00246638,0.8339704,0.0003971117,0.0001804577,0.00006409865,0.0004603458,0.002112438,0.008926715],"genre_scores_gemma":[0.944602,0.0003909121,0.04959236,0.00009270748,0.00003560363,0.00007681418,0.0004336301,0.00004355297,0.004732299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01380623,"threshold_uncertainty_score":0.02745175,"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."}}