{"id":"W3215708800","doi":"10.1109/epec52095.2021.9621650","title":"Wind Speed Forecasting Using ARMA and Neural Network Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Wind power; Autoregressive–moving-average model; Artificial neural network; Recurrent neural network; Computer science; Autoregressive model; Time series; Feedforward neural network; Wind power forecasting; Chaotic; Electric power system; Data modeling; Feed forward; Power (physics); Moving average; Artificial intelligence; Machine learning; Engineering; Meteorology; Control engineering; Statistics; Mathematics; Geography","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":[0.0006048474,0.0006513743,0.0006790772,0.0007172173,0.0002913878,0.000894469,0.0006220739,0.0007887708,0.001034445],"category_scores_gemma":[0.002064465,0.0003337188,0.0006349192,0.001106417,0.0001748188,0.0008633059,0.0002666332,0.0008449745,0.0004321865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004695657,"about_ca_system_score_gemma":0.0004008103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01875537,"about_ca_topic_score_gemma":0.01500887,"domain_scores_codex":[0.999711,0.0000856871,0.00002771457,0.00006976378,0.00007938399,0.0000264009],"domain_scores_gemma":[0.9995099,0.0002617154,0.00007754721,0.0000285054,0.000109247,0.00001296969],"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":[0.00004814118,0.00003627087,0.002083246,0.00005492738,0.00006678465,0.00005293657,0.00001892464,0.9562722,0.000769072,0.001111166,0.0007050997,0.03878121],"study_design_scores_gemma":[9.163911e-7,0.000003843201,0.0002793638,0.000002395743,0.0000028365,0.000002491005,0.000001516521,0.9993012,0.00007804914,0.0002320448,0.0000929084,0.00000243816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3521955,0.004880719,0.6218433,0.001056289,0.0004624128,0.0001067569,0.001159536,0.002318729,0.01597687],"genre_scores_gemma":[0.9576311,0.001098665,0.03662666,0.00004811846,0.00007142958,0.000053893,0.0004853901,0.00004385249,0.003940905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01875537,"threshold_uncertainty_score":0.03729242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05061649448786988,"score_gpt":0.2214827563895116,"score_spread":0.1708662619016417,"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."}}