{"id":"W4409787719","doi":"10.61091/jcmcc127a-524","title":"Quantification of ultra-short-term forecasting performance of wind power based on multivariate LSTM and logistic coupled models","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Term (time); Multivariate statistics; Logistic regression; Econometrics; Computer science; Wind power; Artificial intelligence; Machine learning; Environmental science; Statistics; Mathematics; Engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014701,0.0002496483,0.0007081695,0.0002775494,0.0001171936,0.00005418562,0.0002044989,0.0001467083,0.000001629505],"category_scores_gemma":[0.0002759209,0.0002301006,0.0001041321,0.0002718958,0.00009020866,0.0001475058,0.00004390174,0.0003290864,8.04394e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004504992,"about_ca_system_score_gemma":0.00006642938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004348344,"about_ca_topic_score_gemma":1.543525e-7,"domain_scores_codex":[0.9980214,0.00004486613,0.00120131,0.0001487775,0.0003585615,0.0002250808],"domain_scores_gemma":[0.9978548,0.0009256802,0.0005832517,0.0001891637,0.0003668657,0.0000802541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004908866,0.0007811888,0.002449581,0.003749002,0.0003572724,0.00001175555,0.001948755,0.4293493,0.0309925,0.5265177,0.00002306881,0.003329015],"study_design_scores_gemma":[0.002210514,0.0005612824,0.0003552201,0.002183277,0.00009797323,0.00001136996,0.00008632427,0.9563266,0.009203895,0.02876184,0.00000628852,0.0001954055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689187,0.0001822173,0.02456616,0.000007902403,0.004711362,0.0001560215,0.000003150153,0.00002550545,0.00142895],"genre_scores_gemma":[0.997594,0.00002915838,0.002211564,0.000003498301,0.0001306005,7.873196e-7,0.000001738831,0.00002704256,0.000001610841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5269773,"threshold_uncertainty_score":0.938323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02782414027685679,"score_gpt":0.2489152995590909,"score_spread":0.2210911592822341,"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."}}