{"id":"W4415819927","doi":"10.3390/a18110695","title":"Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production","year":2025,"lang":"en","type":"article","venue":"Algorithms","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperparameter; Bayesian optimization; Renewable energy; Wind power; Electricity generation; Convolutional neural network; Artificial neural network; Electricity; Hyperparameter optimization; Production (economics)","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":[0.0001640023,0.0001272793,0.000141485,0.0001287509,0.0001525571,0.00006370495,0.00005076394,0.00006305961,0.000005437523],"category_scores_gemma":[0.00006678252,0.0001301395,0.00001625322,0.0003951381,0.000008997226,0.0001069832,0.00001914094,0.0001360479,0.000001595647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005369751,"about_ca_system_score_gemma":0.000007874206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009662361,"about_ca_topic_score_gemma":0.0000115,"domain_scores_codex":[0.9993325,0.00002828229,0.0001650468,0.0001934028,0.00007973403,0.0002010481],"domain_scores_gemma":[0.9997641,0.0000271331,0.0000214694,0.00008466058,0.00003029364,0.00007229999],"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":[0.000004906273,0.000006592606,0.0005015614,0.00006734576,0.0000300083,0.000001472327,0.0001061979,0.9679863,0.001149592,0.00006003796,0.0007393078,0.02934672],"study_design_scores_gemma":[0.000143152,0.00003452004,0.00002636692,0.00005827075,0.00001536748,0.00001480141,0.00002399206,0.9888816,0.00324922,0.00001690054,0.007400325,0.0001355156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01409483,0.003166326,0.9785046,0.0001662166,0.0007818546,0.0002286793,0.00000623276,0.0003805429,0.002670723],"genre_scores_gemma":[0.9896653,0.0003651425,0.007645472,0.00003399043,0.0001601168,0.00003961666,0.00006625496,0.00003378317,0.001990268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9755705,"threshold_uncertainty_score":0.5306934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004489227441855999,"score_gpt":0.1934846813343165,"score_spread":0.1889954538924605,"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."}}