{"id":"W2910014246","doi":"10.1016/j.renene.2019.01.049","title":"Prediction of wind power ramp events based on residual correction","year":2019,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Residual; Wind power forecasting; Wind power; Markov chain; Wind speed; Computer science; Numerical weather prediction; Power (physics); Stability (learning theory); Term (time); Electric power system; Control theory (sociology); Reliability engineering; Engineering; Meteorology; Algorithm; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009832321,0.0001328716,0.000153249,0.0001371127,0.00002869249,0.000007722901,0.0000786371,0.0001134326,0.0003605845],"category_scores_gemma":[0.00001613326,0.0001349283,0.00005485319,0.0001936609,0.000008227177,0.00008820631,0.00001053033,0.00007251592,0.00001310262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006128501,"about_ca_system_score_gemma":0.00002289124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004687652,"about_ca_topic_score_gemma":0.00005187776,"domain_scores_codex":[0.9992072,0.00002671221,0.000203725,0.0001577911,0.0002079278,0.0001966572],"domain_scores_gemma":[0.9995823,0.00006219069,0.00004558963,0.000230315,0.00002946248,0.00005017598],"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":[0.00002682496,0.00002386746,0.00164513,0.00001740325,0.00001790365,7.512165e-7,0.00002153707,0.9782147,0.01671326,0.00005410464,0.002591987,0.0006725329],"study_design_scores_gemma":[0.0007848305,0.0003278077,0.002090791,0.0002461728,0.00001440245,0.000004050675,0.00002937697,0.7054863,0.2498931,0.00008293462,0.04082306,0.000217188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.680467,0.0001331312,0.01366501,0.00001643816,0.008022878,0.00009592361,0.00003208849,0.0004701002,0.2970974],"genre_scores_gemma":[0.9948929,0.00001760435,0.0001178356,0.00003590022,0.0001236908,0.000003688327,0.00005731598,0.00003637429,0.004714721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3144259,"threshold_uncertainty_score":0.5502218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007826135737354257,"score_gpt":0.1790574217490179,"score_spread":0.1712312860116637,"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."}}