{"id":"W4385694025","doi":"10.20944/preprints202308.0693.v1","title":"Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"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":"Wind speed; Solar irradiance; Irradiance; Computer science; Random forest; Python (programming language); Support vector machine; Meteorology; Ensemble learning; Ensemble forecasting; Mean absolute percentage error; Algorithm; Simulation; Artificial intelligence; Artificial neural network; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009976752,0.0004337255,0.0005023701,0.0002310683,0.0002276567,0.00004975353,0.0003287886,0.0004060272,0.0000722811],"category_scores_gemma":[0.000435419,0.0005106743,0.0001901675,0.000167888,0.0000579737,0.0001014419,0.0006524088,0.001312565,0.0001040171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328459,"about_ca_system_score_gemma":0.00004498223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00011947,"about_ca_topic_score_gemma":0.00005438579,"domain_scores_codex":[0.9979973,0.000116242,0.0004764444,0.0007664763,0.0001331638,0.0005104017],"domain_scores_gemma":[0.9986586,0.0003634371,0.0001287112,0.0006064398,0.00006227973,0.0001805163],"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":[0.00001867031,0.00001883328,0.04375448,0.0008915695,0.0003767493,0.000006968148,0.001185366,0.9273449,0.01709411,0.0001103902,0.00003754121,0.009160416],"study_design_scores_gemma":[0.0003538458,0.000019881,0.0249213,0.0003991349,0.0001242346,0.00001744631,0.00004925888,0.946943,0.004735747,0.002733808,0.019123,0.0005792936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6748258,0.004133424,0.2966475,0.0002846485,0.008051989,0.001528517,0.0002663187,0.004359497,0.009902278],"genre_scores_gemma":[0.9705502,0.00165225,0.02112945,0.000018409,0.0002883697,0.00008231246,0.0002731833,0.0002513152,0.005754518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2957244,"threshold_uncertainty_score":0.9997345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0959507331151295,"score_gpt":0.3525151933803349,"score_spread":0.2565644602652054,"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."}}