{"id":"W4381194074","doi":"10.11159/ehst23.120","title":"Deep Learning-Based Models for Wind and Solar Curtailment Forecasting","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Energy Harvesting, Storage, and Transfer","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Meteorology; Solar wind; Environmental science; Artificial intelligence; Geography; Physics","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":[0.0002214252,0.0001633242,0.0001906527,0.0001283404,0.0001002739,0.00006562881,0.0002213627,0.000066081,0.000007960795],"category_scores_gemma":[0.00009702652,0.000140315,0.00006960801,0.0001264627,0.00009235905,0.0002612684,0.00003876872,0.0001208769,8.39252e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001459649,"about_ca_system_score_gemma":0.00001932959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005065065,"about_ca_topic_score_gemma":0.00003074303,"domain_scores_codex":[0.9991227,0.000003814665,0.0002828409,0.000185221,0.0002140694,0.000191335],"domain_scores_gemma":[0.9994459,0.0001084596,0.00006487141,0.00003946291,0.000286123,0.00005516427],"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":[0.00009040746,0.00003763957,0.005291929,0.0008120174,0.0002320476,0.000001246334,0.001602288,0.8199921,0.03467194,0.1228327,0.00007899711,0.01435672],"study_design_scores_gemma":[0.0004731524,0.00007038491,0.0003708442,0.0003568052,0.00003117049,0.000004640075,0.0001550368,0.9716271,0.02275657,0.003005286,0.0009976233,0.0001513778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9626363,0.0001504215,0.02938883,0.0001124716,0.0002626437,0.0001013484,0.00001977573,0.0001107375,0.007217495],"genre_scores_gemma":[0.9986725,0.00009595634,0.0007203711,0.00001562884,0.0000554499,0.00001926269,0.00001082496,0.00002851526,0.0003814634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.151635,"threshold_uncertainty_score":0.572188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03519860510791901,"score_gpt":0.2174115865353191,"score_spread":0.1822129814274001,"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."}}