{"id":"W4285495285","doi":"10.5539/eer.v12n2p11","title":"Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study","year":2022,"lang":"en","type":"article","venue":"Energy and Environment Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wind speed; Computer science; Artificial neural network; MATLAB; Wind power; Machine learning; Artificial intelligence; Time series; Software; Data mining; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002275763,0.0002055204,0.0002103758,0.0002268747,0.001178649,0.00005892762,0.0003018305,0.0000560653,0.0003616027],"category_scores_gemma":[0.00005462007,0.0001914791,0.00003169017,0.0002478874,0.00008427475,0.00009598254,0.000811992,0.0008700265,0.000001480643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001371712,"about_ca_system_score_gemma":0.00001226333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006304987,"about_ca_topic_score_gemma":0.000021186,"domain_scores_codex":[0.9973921,0.0006152727,0.0002337104,0.0005337404,0.0006802941,0.0005448966],"domain_scores_gemma":[0.999066,0.0003037269,0.00002797767,0.0004556299,0.00000663238,0.0001400677],"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":[0.00003875428,0.0002179084,0.002778919,0.00001202489,0.000041763,0.001382679,0.0002491821,0.9881368,0.0004169961,0.00003270755,0.00003284313,0.006659443],"study_design_scores_gemma":[0.0005154267,0.0005074359,0.00004689382,0.000004145453,0.00001708603,0.0004577051,0.001460424,0.9863216,0.00007620486,0.00001483882,0.01037973,0.0001984618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946533,0.0003913231,0.001391597,0.00001922025,0.00008717322,0.0001472814,0.00002055519,0.00008216107,0.003207371],"genre_scores_gemma":[0.9981056,0.00002653872,0.001280417,0.0000248775,0.0001023532,0.00001364601,0.0001319456,0.00004797335,0.0002666183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01034688,"threshold_uncertainty_score":0.9065335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1542506160550401,"score_gpt":0.2971544442610145,"score_spread":0.1429038282059744,"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."}}