{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001670443,0.0009487021,0.0007202073,0.000591394,0.0002684002,0.0006554442,0.000807142,0.0008250488,0.001042641],"category_scores_gemma":[0.005198563,0.0005191356,0.0004693457,0.0005673314,0.0003281542,0.0009333618,0.0005238111,0.001169641,0.0003601134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218078,"about_ca_system_score_gemma":0.001355793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02322997,"about_ca_topic_score_gemma":0.02102552,"domain_scores_codex":[0.9995822,0.0001191742,0.00003149374,0.0001234595,0.00007606741,0.00006766194],"domain_scores_gemma":[0.9988368,0.0007025659,0.0001488268,0.00007005243,0.0002100648,0.00003175261],"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":[0.00006111816,0.00004626677,0.001043566,0.00002314535,0.00002901129,0.00001515932,0.00001797066,0.973438,0.0006268021,0.0008856341,0.0003693917,0.02344393],"study_design_scores_gemma":[0.000003675829,0.000009142565,0.0001949848,0.000002893979,0.000002457885,0.000002273031,0.000002035147,0.9990526,0.0002244376,0.0004397182,0.00006395411,0.000001917271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3558499,0.00124579,0.6309826,0.0006115713,0.0001318254,0.0001451791,0.0005021174,0.001764418,0.008766606],"genre_scores_gemma":[0.9593966,0.0001545006,0.03872333,0.00009064396,0.00002040636,0.00007590507,0.0003823474,0.00004659922,0.001109652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02322997,"threshold_uncertainty_score":0.04618955,"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."}}