{"id":"W1517243424","doi":"10.1109/icnn.1994.375040","title":"A decomposition approach to forecasting electric power system commercial load using an artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Backpropagation; Electric power system; Computer science; Set (abstract data type); Electrical load; Power (physics); Artificial intelligence; Machine learning; Data mining; Engineering; Voltage; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002089942,0.0002527056,0.0002524715,0.0001172024,0.000273662,0.0001346732,0.0001597362,0.0001144674,0.0000412361],"category_scores_gemma":[0.000009716737,0.0002598167,0.00007819902,0.0006185903,0.000008933354,0.0002559941,0.00003183352,0.0002133092,0.00002396312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002402448,"about_ca_system_score_gemma":0.000007354313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005717003,"about_ca_topic_score_gemma":0.00003509443,"domain_scores_codex":[0.9984187,0.0000539541,0.0003742724,0.0002680903,0.000228528,0.0006564539],"domain_scores_gemma":[0.9994665,0.0000386296,0.00004012556,0.0001997992,0.00005220376,0.0002026893],"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":[0.00001101681,0.0000447712,0.0001019726,0.00003283147,0.00001874133,0.000008105021,0.0004753002,0.9855757,0.003475271,0.0008206992,0.0004505734,0.008984997],"study_design_scores_gemma":[0.0001171875,0.00007390796,0.00005949011,0.0000502355,0.00001999479,0.0001397939,0.00008613904,0.9981377,0.0008047221,0.00001165413,0.000176547,0.0003226469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8565564,0.0001382308,0.09361311,0.000008169704,0.0007962799,0.0001881287,0.000002194103,0.0007549788,0.04794248],"genre_scores_gemma":[0.9865407,6.721655e-7,0.01229163,0.00008118492,0.0009777097,0.00001281807,0.000009410693,0.00006575845,0.00002016687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1299842,"threshold_uncertainty_score":0.9999854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05050799430647605,"score_gpt":0.2326354485069009,"score_spread":0.1821274542004249,"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."}}