{"id":"W2101924284","doi":"10.1007/s40095-014-0105-5","title":"Application of sliding window technique for prediction of wind velocity time series","year":2014,"lang":"en","type":"article","venue":"International journal of energy and environmental engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Sliding window protocol; Artificial neural network; Perceptron; Mean squared error; Renewable energy; Series (stratigraphy); Time series; Wind power; Computer science; Multilayer perceptron; Grid; Data mining; Wind speed; Reliability (semiconductor); Window (computing); Statistics; Artificial intelligence; Engineering; Power (physics); Machine learning; Mathematics; Meteorology","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":[0.0005513973,0.0003756729,0.0003138671,0.0006028676,0.0001823468,0.0003072766,0.0003198723,0.0003240152,0.000696458],"category_scores_gemma":[0.001487465,0.0001719741,0.0002752101,0.0005042783,0.000101364,0.0006803406,0.0001569822,0.0003969794,0.0001627921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467174,"about_ca_system_score_gemma":0.0003012941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005814339,"about_ca_topic_score_gemma":0.004295117,"domain_scores_codex":[0.9998426,0.00003689964,0.00001590508,0.0000379716,0.00005047662,0.00001618255],"domain_scores_gemma":[0.9997051,0.0001461495,0.00003150103,0.0000249573,0.00008220816,0.000009991993],"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":[0.0003800938,0.0001724481,0.01473935,0.0001670736,0.000153793,0.0003518803,0.000202333,0.4521381,0.04196166,0.001683693,0.001140256,0.4869094],"study_design_scores_gemma":[0.000003183188,0.00008321663,0.003462415,0.0000055867,0.00001124515,0.00002359935,0.00001495268,0.9919074,0.00401979,0.000179226,0.000283549,0.00000586942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4812833,0.0008851111,0.5142114,0.0001292508,0.0001755649,0.00005788981,0.0002231224,0.001080747,0.001953585],"genre_scores_gemma":[0.955127,0.0003086976,0.04371897,0.000007326917,0.00001549055,0.00002431921,0.0001423738,0.00001707445,0.0006387659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005814339,"threshold_uncertainty_score":0.01156098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002843087895573815,"score_gpt":0.1602157319042069,"score_spread":0.1573726440086331,"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."}}