{"id":"W3037903850","doi":"10.1609/aaai.v34i08.7027","title":"PIDS: An Intelligent Electric Power Management Platform","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Science Council; National Natural Science Foundation of China","keywords":"Electricity; Electric power system; Computer science; Consumption (sociology); Process (computing); Power consumption; Power management; Power (physics); Electric power; Demand response; Business; Environmental economics; Operations research; Operations management; Telecommunications; Risk analysis (engineering); Engineering; Economics; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000503618,0.0007481531,0.0003715202,0.0007739994,0.0003290222,0.000829754,0.001328772,0.0003281354,0.007857242],"category_scores_gemma":[0.0008730681,0.0002957687,0.0002457563,0.0006174798,0.0002878628,0.001262669,0.00134108,0.0007151309,0.002554124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008110015,"about_ca_system_score_gemma":0.0009614734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004730019,"about_ca_topic_score_gemma":0.003557803,"domain_scores_codex":[0.9997445,0.0000289152,0.00001774821,0.00006762241,0.00009884928,0.00004244462],"domain_scores_gemma":[0.999692,0.00004271086,0.00003587578,0.00006557952,0.00009265594,0.00007135636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002808536,0.0009584993,0.02469975,0.0006312671,0.0002151842,0.001194353,0.0007706188,0.1116901,0.04697553,0.0199268,0.251924,0.5382053],"study_design_scores_gemma":[0.0003870913,0.000374479,0.01208876,0.00003910572,0.00007211008,0.0001949191,0.0001769053,0.7872575,0.02251784,0.01212489,0.1646622,0.0001041553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.186832,0.001048117,0.3645743,0.001787542,0.0006047075,0.001481489,0.01319665,0.3386791,0.09179603],"genre_scores_gemma":[0.8974984,0.0004745806,0.06502682,0.0005170152,0.000104757,0.0004170733,0.01205575,0.001310038,0.02259555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007857242,"threshold_uncertainty_score":0.02628505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06675763103910648,"score_gpt":0.2556870596545758,"score_spread":0.1889294286154693,"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."}}