{"id":"W7117371263","doi":"10.1016/j.scsadv.2025.100022","title":"Interpretable generalized Gaussian mixture modeling for risk-aware solar power forecasting","year":2025,"lang":"en","type":"article","venue":"Sustainable Cities and Society Advances","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Probabilistic logic; Heteroscedasticity; Probabilistic forecasting; Robustness (evolution); Mixture model; Gaussian; Parameterized complexity; Gaussian process; Bayesian probability","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":[],"consensus_categories":[],"category_scores_codex":[0.0003864714,0.0001732601,0.0002214872,0.00005021732,0.0009925849,0.0003910513,0.0003079781,0.0001032426,0.000007655416],"category_scores_gemma":[0.0001125642,0.0001602127,0.0001715924,0.0002912669,0.00005698386,0.0009859974,0.0001814533,0.000171664,1.756275e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008558972,"about_ca_system_score_gemma":0.0001512771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001014828,"about_ca_topic_score_gemma":0.000009900715,"domain_scores_codex":[0.998786,0.00003833028,0.0002185828,0.0003592828,0.0001182717,0.0004795217],"domain_scores_gemma":[0.999198,0.0001524211,0.00009746437,0.0002311493,0.0002538928,0.00006707687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001403581,0.0001174022,0.003327815,0.002957654,0.0005128279,0.0000179059,0.07170466,0.1224773,0.0000994685,0.7349695,0.02494645,0.03872868],"study_design_scores_gemma":[0.000451996,0.00003837332,0.000009117448,0.00003832798,0.00001446731,0.00000162515,0.02224378,0.8307258,0.0001214233,0.07527877,0.07089795,0.0001783311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01196502,0.007348848,0.9777699,0.000490491,0.0003097929,0.0003697938,0.00001634021,0.0001301381,0.001599718],"genre_scores_gemma":[0.9036478,0.002766969,0.07955422,0.002076799,0.00009849551,0.0001568932,0.00001713346,0.00002172385,0.01165996],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8982157,"threshold_uncertainty_score":0.7634261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007953765955038711,"score_gpt":0.2416686854048815,"score_spread":0.2337149194498428,"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."}}