{"id":"W4285122835","doi":"10.1109/access.2022.3187839","title":"Load Forecasting Techniques for Power System: Research Challenges and Survey","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":298,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Computer science; Electrical load; Electric power system; Electricity; Mean absolute error; Artificial neural network; Artificial intelligence; Power (physics); Machine learning; Operations research; Statistics; Engineering; Mathematics","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":[0.001282381,0.0009443725,0.001089726,0.001906524,0.0003675903,0.001618601,0.001361137,0.001187723,0.002153846],"category_scores_gemma":[0.002836613,0.0005849885,0.000734238,0.004982624,0.0004179147,0.00380351,0.0006120165,0.001277577,0.001214262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004812681,"about_ca_system_score_gemma":0.0008216677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00289618,"about_ca_topic_score_gemma":0.001868271,"domain_scores_codex":[0.9993218,0.0001212666,0.00007109239,0.0001590925,0.0002927184,0.00003400844],"domain_scores_gemma":[0.9983877,0.0008911295,0.00008684256,0.0001046565,0.0004975412,0.00003216698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004042553,0.00007967417,0.002031203,0.003042021,0.00006584642,0.0001027103,0.000137546,0.02589172,0.001187806,0.01760285,0.01278715,0.9370311],"study_design_scores_gemma":[0.0000244015,0.000452635,0.007547432,0.004953014,0.0002718125,0.001640192,0.0009542822,0.3255355,0.00407803,0.08339472,0.5709239,0.000224229],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01001162,0.8185493,0.1473807,0.003835841,0.00125097,0.00009940167,0.0003618214,0.0004016614,0.01810862],"genre_scores_gemma":[0.08450343,0.862544,0.04429026,0.000517701,0.002603431,0.00007996825,0.0006285088,0.00007428817,0.004758448],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00289618,"threshold_uncertainty_score":0.007205307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1900834257854434,"score_gpt":0.3457127124009007,"score_spread":0.1556292866154572,"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."}}