{"id":"W2015195567","doi":"10.1016/j.rser.2013.06.022","title":"A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada","year":2013,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Wind power; Artificial neural network; Particle swarm optimization; Imperialist competitive algorithm; Wind power forecasting; Time series; Hybrid power; Genetic algorithm; Electric power system; Engineering; Power (physics); Computer science; Artificial intelligence; Machine learning; Multi-swarm optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004717938,0.0002126186,0.0007773277,0.0001255774,0.00005730379,0.00001648467,0.0001114361,0.00004905595,0.00004053978],"category_scores_gemma":[0.00006684868,0.0001864275,0.00005693724,0.0002861955,0.00002371861,0.0001605201,0.0000440594,0.00006965551,1.097957e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000441,"about_ca_system_score_gemma":0.0001170751,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6805295,"about_ca_topic_score_gemma":0.2477017,"domain_scores_codex":[0.9985119,0.0001283969,0.0006669211,0.000209744,0.00009099011,0.0003920731],"domain_scores_gemma":[0.9992385,0.0002041011,0.0001366209,0.0002074795,0.000132225,0.00008107852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000498515,0.0002531695,0.002427899,0.001610644,0.0002259373,0.000008204039,0.003784466,0.9661341,0.001617428,0.0007803228,0.004194761,0.01891321],"study_design_scores_gemma":[0.002942803,0.001279451,0.0009990725,0.0006066,0.0001668108,0.00001898573,0.02373283,0.153363,0.02017585,0.0004908526,0.7954132,0.000810589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.960635,0.02013375,0.00784299,0.00001022077,0.0001802958,0.001125375,0.000007794099,0.00001892669,0.01004565],"genre_scores_gemma":[0.992619,0.0008102801,0.002168198,0.00001356613,0.00002654213,0.000125823,0.00001613842,0.00002393672,0.004196492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8127711,"threshold_uncertainty_score":0.7660258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467074761912558,"score_gpt":0.2619024139922946,"score_spread":0.247231666373169,"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."}}