{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001314971,0.0008683975,0.0009887627,0.0008112221,0.0002743145,0.0005998074,0.0007423554,0.0004084829,0.0008260105],"category_scores_gemma":[0.002193419,0.0002400439,0.0007013458,0.000943956,0.0001202811,0.0009359524,0.0004348102,0.0008062615,0.0002955685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003018421,"about_ca_system_score_gemma":0.0004821814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007187579,"about_ca_topic_score_gemma":0.007490905,"domain_scores_codex":[0.9995829,0.00009701639,0.00003149126,0.0001046778,0.0001350845,0.00004873283],"domain_scores_gemma":[0.9992927,0.0003231091,0.0000617558,0.00008861959,0.0002152042,0.00001851395],"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":[0.00008505473,0.00009002807,0.004933672,0.00005107766,0.0002457882,0.00004661917,0.00004335483,0.7783261,0.001938348,0.001141698,0.0009054391,0.2121928],"study_design_scores_gemma":[0.000001986313,0.00002649008,0.0009340189,0.000005814854,0.0000181976,0.00001048123,0.000007002423,0.9976919,0.000483867,0.0004797269,0.0003356426,0.000004987607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1596511,0.002183371,0.8323925,0.0002214194,0.0002261842,0.00006467561,0.0003587907,0.001186008,0.003716001],"genre_scores_gemma":[0.9201299,0.0008939704,0.07587302,0.00005463241,0.0001185879,0.0000923421,0.0007435838,0.00006676522,0.002027097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007187579,"threshold_uncertainty_score":0.01429147,"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."}}