{"id":"W4391687937","doi":"10.1049/gtd2.13130","title":"Guest Editorial: Artificial intelligence‐empowered reliable forecasting for energy sectors","year":2024,"lang":"en","type":"editorial","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Energy sector; Artificial intelligence; Energy (signal processing); Operations research; Data science; Machine learning; Engineering; Environmental economics; Economics","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":[0.006264248,0.003455646,0.003098803,0.003383381,0.002257511,0.009885795,0.003336785,0.01017267,0.02352618],"category_scores_gemma":[0.01967307,0.0009850899,0.00269146,0.001305359,0.00202041,0.004864254,0.001781945,0.01218498,0.01636936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890203,"about_ca_system_score_gemma":0.00204016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003727226,"about_ca_topic_score_gemma":0.0006922378,"domain_scores_codex":[0.9952089,0.0006616549,0.0006337981,0.0009611418,0.002042943,0.0004915291],"domain_scores_gemma":[0.9746719,0.008920074,0.001681143,0.0006719971,0.01031284,0.003742134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009428506,0.00002951068,0.00008600871,0.0003986135,0.00002820689,0.0001811156,0.00002441622,0.00006984954,0.00017453,0.0005105161,0.9911362,0.007266654],"study_design_scores_gemma":[0.00009293914,0.0001062255,0.0006412528,0.0004787885,0.00007138087,0.0004864968,0.00008532935,0.0005474007,0.0004774066,0.00181133,0.9951632,0.00003832395],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001147924,0.003922159,0.0002346854,0.02536411,0.9689236,0.00001928581,0.00006858872,0.00008055856,0.001272148],"genre_scores_gemma":[0.0007146046,0.003178508,0.00009465176,0.006990435,0.9852934,0.00001352989,0.00003784513,0.00005121794,0.00362577],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.02352618,"threshold_uncertainty_score":0.07870293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577477872660308,"score_gpt":0.2524958975553748,"score_spread":0.2267211188287717,"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."}}