{"id":"W2547978431","doi":"10.1109/ccece.2016.7726749","title":"Baseline load forecasting using a Bayesian approach","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Baseline (sea); Computer science; Flexibility (engineering); Bidding; Procurement; Bayesian probability; Scheduling (production processes); Demand response; Key (lock); Operations research; Machine learning; Artificial intelligence; Electricity; Engineering; Operations management","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":[],"consensus_categories":[],"category_scores_codex":[0.0002142553,0.0001194099,0.00009715369,0.0000613822,0.00003026519,0.00001694857,0.00009817802,0.00003345711,0.0002092185],"category_scores_gemma":[0.00003233503,0.00008206851,0.00003586616,0.0001287253,0.0000124295,0.0001204948,0.00004614734,0.00003374817,0.00003045626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001569848,"about_ca_system_score_gemma":0.000008356797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002436461,"about_ca_topic_score_gemma":0.00001155253,"domain_scores_codex":[0.999289,0.0000112645,0.0001511923,0.0001483918,0.000144399,0.0002557203],"domain_scores_gemma":[0.9996634,0.00003416906,0.00001166143,0.0002099247,0.00002078713,0.00006002067],"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":[0.000006603045,0.00003333844,0.001149761,0.0001019648,0.0001135988,0.00001753361,0.00007658033,0.8991488,0.01072893,0.002730503,0.009875762,0.07601665],"study_design_scores_gemma":[0.0002335728,0.000004603494,0.00005433901,0.00002448873,0.000009925358,0.000008149151,0.00002021152,0.9879808,0.001917157,0.00005612583,0.009533558,0.0001570765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01328228,0.00003332461,0.8988013,0.00004092657,0.0002343594,0.00006256506,0.000001039166,0.0004556082,0.08708864],"genre_scores_gemma":[0.9187143,0.00000628404,0.07983506,0.00004762642,0.0002769572,0.00001008818,0.000001292685,0.00004193878,0.001066445],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.905432,"threshold_uncertainty_score":0.3346657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011486249896716,"score_gpt":0.1999349660073043,"score_spread":0.1698201035083371,"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."}}