{"id":"W4402197611","doi":"10.32920/26866597.v1","title":"Machine Learning Prediction of Short-Term Solar PV and Wind Farm Power Generation in Ontario","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Term (time); Environmental science; Photovoltaic system; Wind power; Solar power; Meteorology; Solar wind; Power (physics); Computer science; Engineering; Electrical engineering; Physics; Astronomy; Thermodynamics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711829,0.0002103139,0.000232698,0.0001935789,0.00002238293,0.00005644022,0.00005891812,0.00024968,0.0001218033],"category_scores_gemma":[0.000006911662,0.0002093052,0.00005244869,0.00005525241,0.00001336357,0.00004202982,0.0001884216,0.001081129,0.000001902702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416392,"about_ca_system_score_gemma":0.00003718825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005800377,"about_ca_topic_score_gemma":0.07635467,"domain_scores_codex":[0.9991176,0.00001739338,0.0003404744,0.0002555239,0.0001189623,0.0001500165],"domain_scores_gemma":[0.9997767,0.0000122979,0.00002572362,0.0001277774,0.00001891944,0.00003864002],"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":[0.000005055819,0.00001357785,0.0687145,0.0002625665,0.00009815695,0.00001411662,0.002795084,0.9124827,0.01050057,0.0003745957,0.00003779903,0.004701249],"study_design_scores_gemma":[0.0002142813,0.00006783963,0.02358995,0.0005173507,0.00006268428,0.00001774194,0.00003925851,0.9663697,0.005293214,0.000243805,0.003220409,0.000363789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759322,0.001332968,0.001165873,0.000006748236,0.001023041,0.0001120662,0.00001832334,0.0001762406,0.02023254],"genre_scores_gemma":[0.9978375,0.0001399652,0.0005180465,0.000003565759,0.0001109699,0.000006743685,0.0002897729,0.00004141381,0.001051989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0705543,"threshold_uncertainty_score":0.9404994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855641787042426,"score_gpt":0.2079563449387485,"score_spread":0.1893999270683243,"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."}}