{"id":"W4410050098","doi":"10.1016/j.eswa.2025.127872","title":"Sequential methods for error correction of probabilistic wind power forecasts","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Probabilistic logic; Wind power; Forecast error; Wind power forecasting; Power (physics); Artificial intelligence; Machine learning; Econometrics; Electric power system; Mathematics; Electrical engineering","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":[0.0001651371,0.0001333059,0.0002081832,0.0001040069,0.00009380788,0.00002175236,0.0001110445,0.00007818612,0.000006789986],"category_scores_gemma":[0.00002314475,0.0001162345,0.00005180977,0.0003101634,0.00003767872,0.00005880802,0.00001153866,0.00006382239,0.000001865652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007068514,"about_ca_system_score_gemma":0.00004053019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000479689,"about_ca_topic_score_gemma":0.00001562822,"domain_scores_codex":[0.9992681,0.00002658829,0.0002957967,0.0001786479,0.00006378479,0.0001670897],"domain_scores_gemma":[0.9993451,0.000168704,0.0000632923,0.0002668401,0.0001151645,0.0000408952],"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":[0.0001359009,0.0002225579,0.0002879316,0.001721909,0.000676931,6.35043e-7,0.00334709,0.7397568,0.07095639,0.07883643,0.01695412,0.08710329],"study_design_scores_gemma":[0.000653471,0.0001018715,0.00006068625,0.0004598494,0.00006022277,0.0000181356,0.0005818995,0.5202343,0.01971037,0.0003597169,0.4574162,0.0003432313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001860029,0.001036658,0.981084,0.000020258,0.001090253,0.001164622,0.00001526213,0.0002055093,0.01352344],"genre_scores_gemma":[0.9589163,0.000005982608,0.03694447,0.00001301451,0.0001302167,0.002694375,0.00003746065,0.00003726884,0.001220932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9570563,"threshold_uncertainty_score":0.4739905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932661802728094,"score_gpt":0.3107841474797758,"score_spread":0.2914575294524949,"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."}}