{"id":"W4405324502","doi":"10.3934/gf.2024027","title":"Developing a machine learning model for fast economic optimization of solar power plants using the hybrid method of firefly and genetic algorithms, case study: optimizing solar thermal collector in Calgary, Alberta","year":2024,"lang":"en","type":"article","venue":"Green Finance","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Firefly algorithm; Firefly protocol; Genetic algorithm; Computer science; Power (physics); Solar power; Thermal; Concentrated solar power; Optimization algorithm; Engineering; Mathematical optimization; Algorithm; Solar energy; Mathematics; Machine learning; Electrical engineering; Geography; Biology; Meteorology; Physics; Particle swarm optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006770926,0.0007183313,0.0007304946,0.0004408419,0.0007691972,0.001098454,0.0008283254,0.00137013,0.001809244],"category_scores_gemma":[0.0009894775,0.000491237,0.0006883781,0.0005450399,0.0006374181,0.0004020686,0.0005159535,0.001000749,0.0001591539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002302879,"about_ca_system_score_gemma":0.002535799,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1417835,"about_ca_topic_score_gemma":0.1129052,"domain_scores_codex":[0.9998691,0.00004204612,0.000005789311,0.00001851951,0.0000349456,0.00002955624],"domain_scores_gemma":[0.9996629,0.0002409225,0.00002473683,0.000007105384,0.00005264839,0.00001159845],"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":[0.000008148095,0.000009956803,0.0001675267,0.00000970264,0.000003523433,0.00002240212,0.000008380161,0.9974088,0.0001796629,0.0003300289,0.00008270385,0.001769193],"study_design_scores_gemma":[0.000002366271,0.000005765681,0.00005431069,0.000001225021,0.000001185324,0.000001380268,0.000004394446,0.9997043,0.00007891736,0.00007064121,0.00007438911,0.000001020504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3523955,0.0006881774,0.6200625,0.0008446091,0.00008858855,0.0003237018,0.0002350128,0.0006857782,0.02467622],"genre_scores_gemma":[0.9366896,0.0002671053,0.05439779,0.00006517654,0.00001557923,0.0002691651,0.0001324213,0.00004607766,0.008117085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8582165,"threshold_uncertainty_score":0.2819164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252154622523411,"score_gpt":0.2841994474253476,"score_spread":0.2516779012001135,"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."}}