{"id":"W4388042065","doi":"10.3390/atmos14111635","title":"Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions","year":2023,"lang":"en","type":"article","venue":"Atmosphere","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Solar irradiance; Wind speed; Mean squared error; Mean absolute percentage error; Computer science; Ensemble forecasting; Irradiance; Ensemble learning; Random forest; Metric (unit); Meteorology; Support vector machine; Performance metric; Artificial neural network; Environmental science; Machine learning; Mathematics; Statistics; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001908629,0.0001217779,0.000134638,0.000006308908,0.0001665545,0.00003184255,0.00006056461,0.00007339864,0.00003466367],"category_scores_gemma":[0.00006092942,0.000128345,0.00004360098,0.0002249642,0.00001817634,0.00009324367,0.00002257862,0.0001656021,0.00001817672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002370891,"about_ca_system_score_gemma":0.000007841116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002561281,"about_ca_topic_score_gemma":0.00004316232,"domain_scores_codex":[0.9993885,0.00002372955,0.0001262116,0.0001529932,0.00004416244,0.0002644676],"domain_scores_gemma":[0.9996127,0.0001795247,0.00001893992,0.0001028629,0.00001470148,0.00007123297],"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":[0.000005618053,0.000004053979,0.001929943,0.0001111524,0.00007142724,0.000004507646,0.0004647652,0.8644015,0.005106533,0.0002277919,0.0007523975,0.1269203],"study_design_scores_gemma":[0.0002215912,0.00003304603,0.001086191,0.0000369659,0.00001809259,0.000009214703,0.00007408299,0.9251449,0.0002832063,0.0003885202,0.07257312,0.0001310838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6093789,0.01056772,0.3549747,0.0002534925,0.002750272,0.000488847,0.00005746764,0.003347598,0.01818103],"genre_scores_gemma":[0.9163586,0.0006960937,0.07563934,0.00002499244,0.0001346846,0.00001035825,0.00007154648,0.00009729862,0.006967117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3069797,"threshold_uncertainty_score":0.5233755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0134850626987397,"score_gpt":0.2668117107798162,"score_spread":0.2533266480810765,"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."}}