{"id":"W4387703332","doi":"10.2174/0123520965264083230926105355","title":"Predicting Solar PV Output based on Hybrid Deep Learning and Physical Models: Case Study of Morocco","year":2023,"lang":"en","type":"article","venue":"Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Rimouski","funders":"","keywords":"Renewable energy; Intermittency; Photovoltaic system; Meteorology; Probabilistic forecasting; Computer science; Grid; Solar power; Numerical weather prediction; Environmental science; Power (physics); Engineering; Artificial intelligence; Mathematics; Electrical engineering; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003755848,0.0007328962,0.0004377736,0.0004549369,0.0003979836,0.0005688179,0.0006165683,0.0007065133,0.0009930431],"category_scores_gemma":[0.0006371427,0.0001649682,0.0004421452,0.0004254253,0.0002649313,0.0003225635,0.0003870741,0.0004641422,0.000144368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151651,"about_ca_system_score_gemma":0.0005392837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1087703,"about_ca_topic_score_gemma":0.1040474,"domain_scores_codex":[0.9999007,0.00002505783,0.000007321127,0.00002564032,0.00001747314,0.0000238485],"domain_scores_gemma":[0.9996941,0.0001706554,0.0000227339,0.00002177513,0.00006192896,0.00002871769],"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":[0.0002666711,0.0002458094,0.04551198,0.0001476203,0.0001269877,0.00284967,0.0001352091,0.925363,0.002577217,0.0006002153,0.001427067,0.02074857],"study_design_scores_gemma":[0.00001557895,0.00004801548,0.0153946,0.000009494722,0.0000248749,0.00005682313,0.00009408224,0.9825127,0.001196327,0.0002210322,0.000414761,0.00001171918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933407,0.0002768502,0.003528014,0.0002656923,0.00002185141,0.00001945745,0.0004228,0.0001128626,0.002011712],"genre_scores_gemma":[0.9981372,0.00006510089,0.0009409761,0.00001282596,0.000005613859,0.000006300874,0.0002114306,0.000004655642,0.0006158497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1087703,"threshold_uncertainty_score":0.2162744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009070390562322034,"score_gpt":0.232683644695446,"score_spread":0.223613254133124,"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."}}