{"id":"W2133604230","doi":"10.1109/pes.2011.6039625","title":"Wind power ramp events classification and forecasting: A data mining approach","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wind power; Support vector machine; Wind power forecasting; Data mining; Computer science; Data set; Data modeling; Wind speed; Classifier (UML); Set (abstract data type); Machine learning; Power (physics); Electric power system; Artificial intelligence; Meteorology; Engineering; Database; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001030708,0.0007516182,0.001188896,0.00299182,0.000527997,0.001463006,0.001131876,0.0009933269,0.0007748089],"category_scores_gemma":[0.002533119,0.0003678588,0.0008025966,0.002999659,0.0002930969,0.001340266,0.0005387738,0.0009546662,0.0004763537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004354479,"about_ca_system_score_gemma":0.0006818654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004902797,"about_ca_topic_score_gemma":0.005110867,"domain_scores_codex":[0.9994076,0.00008730381,0.00008521053,0.0001289585,0.0002384866,0.00005243814],"domain_scores_gemma":[0.9991518,0.0003899114,0.00009165924,0.00009649077,0.0002263058,0.00004376282],"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":[0.0002594366,0.0005819076,0.02159625,0.0003834619,0.0002303649,0.0003741942,0.000209451,0.1416841,0.00705461,0.005661091,0.008660813,0.8133044],"study_design_scores_gemma":[0.00002358462,0.0001055635,0.005274553,0.00006065117,0.00005244413,0.000152359,0.0001300069,0.9798752,0.003382305,0.006542625,0.004374009,0.00002664174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0727236,0.001260591,0.9176811,0.001259672,0.000152182,0.0004241796,0.002288311,0.001519734,0.002690638],"genre_scores_gemma":[0.4647802,0.001432711,0.5270648,0.0001488922,0.0003010664,0.0003585337,0.003904058,0.00005286766,0.001956969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004902797,"threshold_uncertainty_score":0.009748518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1478967254327655,"score_gpt":0.2393939968857752,"score_spread":0.09149727145300965,"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."}}