{"id":"W3184434808","doi":"10.1109/icjece.2021.3076124","title":"Short-Term Load Forecasting for Jordan Power System Based on NARX-ELMAN Neural Network and ARMA Model","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonlinear autoregressive exogenous model; Artificial neural network; Autoregressive–moving-average model; Autoregressive model; MATLAB; Computer science; Backpropagation; Term (time); Electric power system; Moving average; Power (physics); Toolbox; Artificial intelligence; Machine learning; Econometrics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000138867,0.0001967696,0.0002811131,0.0001396633,0.0001046573,0.000112359,0.0000896964,0.00007613141,0.000001237725],"category_scores_gemma":[0.00002383197,0.0001952925,0.00008723993,0.0001870439,0.0000102103,0.00008791083,0.000009446613,0.0002980557,8.741902e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000139988,"about_ca_system_score_gemma":0.0001240659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008392882,"about_ca_topic_score_gemma":0.00006661512,"domain_scores_codex":[0.9989542,0.00001059725,0.0003037192,0.0001431346,0.0001067876,0.0004815214],"domain_scores_gemma":[0.9991314,0.0001486379,0.0000317459,0.00007829592,0.000092944,0.0005169399],"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":[0.000006494151,0.00000260555,0.0004586758,0.00008567356,0.00003531506,0.0002012501,0.00005420462,0.9877732,0.00009245554,0.000763019,0.0002074194,0.01031963],"study_design_scores_gemma":[0.0002929946,0.0001308875,0.0004904103,0.0003033089,0.00002257727,0.0005406726,0.000003266663,0.9973289,0.0001334924,0.00001916362,0.0005280495,0.0002062937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6305624,0.002606208,0.3655778,0.00004790715,0.0008338492,0.00007760056,0.000006932091,0.00005725678,0.0002300226],"genre_scores_gemma":[0.9915727,0.000007650005,0.007796583,0.00006954838,0.0005025988,0.000002819983,0.000002529068,0.00003870506,0.000006838743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3610103,"threshold_uncertainty_score":0.7963797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167559516160001,"score_gpt":0.1730997997972303,"score_spread":0.1614242046356303,"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."}}