{"id":"W3215677444","doi":"10.1109/epec52095.2021.9621686","title":"Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Support vector machine; Autoregressive integrated moving average; Mean absolute percentage error; Wind power; Mean squared error; Time series; Artificial neural network; Intermittency; Computer science; Moving average; Wind power forecasting; Autoregressive–moving-average model; Autoregressive model; Artificial intelligence; Machine learning; Control theory (sociology); Electric power system; Power (physics); Engineering; Statistics; 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":[0.0009663132,0.0007764229,0.0009220901,0.001478619,0.0001940146,0.0007905817,0.0005078319,0.0006774766,0.0007459643],"category_scores_gemma":[0.003043673,0.0002699626,0.000547585,0.00260088,0.0002673102,0.001467815,0.0002323539,0.0006200265,0.0006406689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002864272,"about_ca_system_score_gemma":0.0005234423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003155936,"about_ca_topic_score_gemma":0.003630754,"domain_scores_codex":[0.9992976,0.0001368473,0.00008001084,0.0001240326,0.0003349253,0.00002644687],"domain_scores_gemma":[0.9989631,0.0004953516,0.0001801633,0.0001331931,0.0002161519,0.00001197829],"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":[0.0001376601,0.0001690418,0.01857219,0.0006176691,0.0002663042,0.0001300883,0.00005580305,0.2972588,0.01027646,0.004985627,0.002858334,0.6646721],"study_design_scores_gemma":[0.00001861818,0.0001146627,0.01151289,0.0000722065,0.000048852,0.00009550011,0.00003779371,0.9755269,0.004408496,0.004299481,0.003827214,0.00003744399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09764448,0.00590383,0.8879908,0.0003214285,0.0004027047,0.0001137683,0.0008597914,0.00124785,0.005515366],"genre_scores_gemma":[0.7476698,0.006750421,0.2406688,0.0001186221,0.0003529558,0.0001579718,0.001472807,0.00008891557,0.002719798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003155936,"threshold_uncertainty_score":0.006275177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02718778762838693,"score_gpt":0.2872174824199047,"score_spread":0.2600296947915178,"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."}}