{"id":"W3212952282","doi":"10.1109/access.2021.3126747","title":"A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Wind speed; Real-time computing; Artificial intelligence; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003064627,0.0004545548,0.000522903,0.0005427129,0.0002651592,0.0003970903,0.0006859717,0.0004214164,0.0008698447],"category_scores_gemma":[0.0004931723,0.0002128886,0.0004457063,0.0006902951,0.0001161534,0.0008152114,0.0002855473,0.0004835704,0.0002715673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002965797,"about_ca_system_score_gemma":0.0003938593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005620713,"about_ca_topic_score_gemma":0.008027649,"domain_scores_codex":[0.9998378,0.00001802876,0.00001255339,0.00003957677,0.00007516069,0.00001687451],"domain_scores_gemma":[0.9998441,0.00003677231,0.00002140484,0.00001510912,0.00007437234,0.00000824459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002439173,0.0001371505,0.002805462,0.0001031453,0.0001453409,0.0001741269,0.00006794914,0.317815,0.02565263,0.002903129,0.003499426,0.6464528],"study_design_scores_gemma":[0.000005283126,0.00002694424,0.0005834519,0.000003763421,0.00001272657,0.00003355123,0.000004254672,0.9969128,0.001564072,0.000233224,0.0006135599,0.000006180754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07800274,0.001052114,0.9137815,0.0002202787,0.0002254339,0.00005292152,0.0001896422,0.001097523,0.005377902],"genre_scores_gemma":[0.8105696,0.0006568002,0.1820121,0.0001291706,0.00009768276,0.00008136597,0.0003984162,0.00005069184,0.006004087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005620713,"threshold_uncertainty_score":0.01117599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04177416883366736,"score_gpt":0.2583673391690957,"score_spread":0.2165931703354284,"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."}}