{"id":"W4401879017","doi":"10.1109/compsac61105.2024.00276","title":"Neural Network Fuzzy Electricity Demand Forecasts Based on Fuzzy Inputs","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy logic; Computer science; Artificial neural network; Electricity; Neuro-fuzzy; Electricity demand; Fuzzy control system; Artificial intelligence; Electricity generation; Power (physics); Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004916935,0.0003622615,0.0003220463,0.0003638276,0.0001766597,0.0005208937,0.0004218623,0.0003937306,0.001225004],"category_scores_gemma":[0.002967348,0.0001950202,0.0002734931,0.0003372962,0.0001808997,0.0006261918,0.0001808114,0.0005234954,0.0002133344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008328291,"about_ca_system_score_gemma":0.0003747213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02147648,"about_ca_topic_score_gemma":0.0206595,"domain_scores_codex":[0.9998249,0.00004715637,0.00001129376,0.00003260544,0.00006331172,0.00002073524],"domain_scores_gemma":[0.9993319,0.0004115842,0.00005591071,0.00002584517,0.0001598919,0.00001496113],"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":[0.00007755481,0.00002049522,0.0011754,0.00002398923,0.00001656104,0.00003625559,0.00002117715,0.9765329,0.0008112987,0.001901209,0.0005054164,0.01887776],"study_design_scores_gemma":[0.000001826563,0.000003621238,0.0002800241,0.000002007661,0.000001247553,0.000001673255,0.000001884313,0.998953,0.0001461294,0.0005584186,0.00004816429,0.000001911597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5131245,0.0005315306,0.4692497,0.0005328825,0.0001607948,0.00005489089,0.0006818987,0.0006445828,0.01501922],"genre_scores_gemma":[0.9829029,0.0001165984,0.01506321,0.00002396992,0.00001802226,0.00001778664,0.0001949278,0.000009037751,0.001653418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02147648,"threshold_uncertainty_score":0.04270291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093205126162677,"score_gpt":0.2120092745831197,"score_spread":0.201077223321493,"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."}}