{"id":"W7115927221","doi":"10.34658/9788367934886.w8.4.719-743","title":"PROGNOZOWANIE SZEREGÓW CZASOWYCH ZA POMOCĄ GŁĘBOKO UCZONYCH SZTUCZNYCH SIECI NEURONOWYCH NA PRZYKŁADZIE RYNKU ENERGII ELEKTRYCZNEJ","year":2025,"lang":"","type":"article","venue":"Wydawnictwo Politechniki Łódzkiej","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Energy (signal processing); Transmission network; New energy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841899,0.0007746126,0.0008249961,0.0015464,0.001220513,0.005229068,0.001202371,0.001514523,0.03151261],"category_scores_gemma":[0.006488663,0.000495858,0.001278697,0.001770411,0.00135841,0.00346697,0.002962534,0.002203674,0.004655326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003958884,"about_ca_system_score_gemma":0.006629038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0347264,"about_ca_topic_score_gemma":0.03424567,"domain_scores_codex":[0.9986053,0.0002005812,0.0001196691,0.0003631163,0.0003722148,0.0003390879],"domain_scores_gemma":[0.9975976,0.000574421,0.0004602913,0.0002602308,0.0007491098,0.000358279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001416004,0.000455118,0.4304163,0.002064332,0.001060415,0.003582882,0.005111541,0.1064939,0.008864484,0.1519918,0.0580614,0.2304819],"study_design_scores_gemma":[0.0002629058,0.0006264108,0.4030384,0.001360417,0.0009009663,0.00300396,0.01790079,0.08598008,0.007666584,0.2908036,0.1879327,0.0005231762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6510757,0.007911696,0.1261969,0.02796061,0.0007959401,0.0006058679,0.02344095,0.00171538,0.160297],"genre_scores_gemma":[0.9545631,0.003657523,0.008177788,0.0005176347,0.0000896168,0.0002673038,0.005004634,0.0002235348,0.02749886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0347264,"threshold_uncertainty_score":0.1054202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008932960990674262,"score_gpt":0.2367177157817549,"score_spread":0.2277847547910806,"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."}}