{"id":"W1983664186","doi":"10.1016/j.asoc.2011.07.001","title":"Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm","year":2011,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Algorithm; Overfitting; Computer science; Mean squared error; Monte Carlo method; Bayesian probability; Feedforward neural network; Support vector machine; Artificial intelligence; Machine learning; Mathematics; Statistics","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":[0.0003366958,0.0004946104,0.0004508928,0.00008954418,0.0003904742,0.0001126534,0.0003575767,0.0001546243,0.00001804065],"category_scores_gemma":[0.00001442722,0.0005605287,0.0001358315,0.0002742569,0.00006466285,0.0001574797,0.0001868352,0.0006317188,0.000004115171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001504661,"about_ca_system_score_gemma":0.00001822372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009687,"about_ca_topic_score_gemma":0.000009403532,"domain_scores_codex":[0.9975302,0.00002916988,0.000601357,0.000511258,0.0002820683,0.001045965],"domain_scores_gemma":[0.9991537,0.0001292083,0.0001087416,0.0003260133,0.00005352157,0.0002288353],"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":[0.000007226351,0.00001512493,0.0008962195,0.00003151494,0.00006280101,0.00004441153,0.0007514546,0.5848009,0.000647021,0.00002112779,0.0000923417,0.4126298],"study_design_scores_gemma":[0.000291139,0.00002000561,0.00008848197,0.00008597563,0.00003985118,0.00009530009,0.0001009187,0.9971863,0.001345607,0.00004555979,0.0001034334,0.0005974538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4189955,0.00048155,0.5743665,0.000001604739,0.0006890713,0.0001592802,0.000005446145,0.000827461,0.004473635],"genre_scores_gemma":[0.9671782,0.00000664824,0.03202882,0.0000537597,0.0005223356,0.000006825539,0.00001973235,0.0001666409,0.00001701071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5481828,"threshold_uncertainty_score":0.9996846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02906903108889821,"score_gpt":0.2143056865226848,"score_spread":0.1852366554337866,"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."}}