{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002774357,0.0006381256,0.0005344371,0.00064254,0.0001635957,0.0003835495,0.0003606607,0.0004063426,0.0008296521],"category_scores_gemma":[0.001022773,0.0001818989,0.0002934509,0.0004149291,0.000132628,0.000431315,0.0001753423,0.0006596243,0.0003195507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001414451,"about_ca_system_score_gemma":0.0002072567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005378945,"about_ca_topic_score_gemma":0.00572705,"domain_scores_codex":[0.9998717,0.00001592149,0.00001033616,0.00003632432,0.00004003472,0.00002556086],"domain_scores_gemma":[0.9995261,0.0001648795,0.00005666116,0.00005379516,0.0001576663,0.00004086452],"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":[0.001252336,0.0002656712,0.03794541,0.0001373161,0.0001150125,0.000562071,0.00005037317,0.8047984,0.02212893,0.000936496,0.003176267,0.1286317],"study_design_scores_gemma":[0.000006651662,0.00003051742,0.005653913,0.000002871,0.00001029155,0.00001376168,0.000003754596,0.9927679,0.001334508,0.00009063578,0.00008011771,0.000005009211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9264204,0.0003447039,0.06709021,0.0001222998,0.0002731556,0.00003842108,0.0007715197,0.001632287,0.003307051],"genre_scores_gemma":[0.995128,0.00005192449,0.004087774,0.000005627984,0.00001893193,0.000003257411,0.0003757031,0.00001499893,0.0003136143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005378945,"threshold_uncertainty_score":0.01069522,"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."}}