{"id":"W2150462782","doi":"10.1109/ptc.2009.5282220","title":"Application of generalized neuron in electricity price forecasting","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bidding; Computer science; Artificial neural network; Electricity market; Data modeling; Electricity price forecasting; Electricity; Function (biology); Artificial intelligence; Data set; Relation (database); Econometrics; Machine learning; Data mining; Economics; Microeconomics; Engineering","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":[0.0004020851,0.0003364371,0.0004349115,0.0004292774,0.000179537,0.0004678571,0.0004731782,0.0005244066,0.0007172419],"category_scores_gemma":[0.00138784,0.0001486975,0.0003340266,0.000920589,0.0002876897,0.0004909449,0.0003129781,0.0003477639,0.0001583524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005359838,"about_ca_system_score_gemma":0.000328449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02011644,"about_ca_topic_score_gemma":0.009680857,"domain_scores_codex":[0.9998148,0.00006376396,0.00001037611,0.00003043696,0.00005842165,0.00002219964],"domain_scores_gemma":[0.9997703,0.0001060744,0.0000158839,0.00002545013,0.00007356308,0.000008731605],"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":[0.0001062587,0.00002008861,0.003608669,0.00005296177,0.00004849295,0.0001919994,0.00006061624,0.8932431,0.003165932,0.004239577,0.0006326516,0.09462967],"study_design_scores_gemma":[0.000003385996,0.00001342312,0.0006829739,0.00000323361,0.000004843716,0.00002677904,0.000008130223,0.9963722,0.0004749066,0.002059369,0.0003458192,0.000004812911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3636672,0.002228168,0.6167797,0.0007234869,0.0002870593,0.00007777137,0.0002608516,0.001346353,0.01462931],"genre_scores_gemma":[0.9590495,0.0005669732,0.03831748,0.00003889734,0.00002290269,0.0000163604,0.00008407445,0.00002117952,0.001882792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02011644,"threshold_uncertainty_score":0.03999871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123320764217805,"score_gpt":0.2086975748156488,"score_spread":0.1963654983938683,"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."}}