{"id":"W2969550468","doi":"10.1109/tia.2019.2936330","title":"Bottom-Up Load Forecasting With Markov-Based Error Reduction Method for Aggregated Domestic Electric Water Heaters","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Energie NB Power (Canada); University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Siemens","keywords":"Mean squared error; Mean absolute percentage error; Computer science; Benchmark (surveying); Markov chain; Particle swarm optimization; Mathematical optimization; Electricity; Electrical load; Approximation error; Reduction (mathematics); Statistics; Algorithm; Engineering; Mathematics; Voltage; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000209158,0.0002949405,0.0002215272,0.0003150536,0.0002333097,0.00005138337,0.0001860129,0.0002939155,0.0001450833],"category_scores_gemma":[0.000001798727,0.0002614471,0.00009600563,0.0007273474,0.00002757128,0.0001537645,0.000001015165,0.0006470617,0.00007281804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004517725,"about_ca_system_score_gemma":0.00006825204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003988197,"about_ca_topic_score_gemma":0.00001081354,"domain_scores_codex":[0.9985122,0.00003328768,0.0003116965,0.0004364258,0.0002336849,0.0004726825],"domain_scores_gemma":[0.9990823,0.0001076574,0.0000485227,0.0005333139,0.0001118914,0.0001163313],"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":[0.00008642403,0.00007360372,0.000004848864,0.00008663672,0.0001404271,7.146716e-7,0.00004873757,0.9646385,0.02257946,0.00002456456,0.0005224621,0.01179366],"study_design_scores_gemma":[0.001546979,0.0001903036,0.00002882897,0.00007115889,0.0002531778,0.00004782648,0.0001089883,0.6611002,0.3272498,0.00004655374,0.008835403,0.0005207444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05183871,0.000009464291,0.9443848,0.0003954253,0.000546163,0.001587487,0.00003449178,0.000619091,0.0005844012],"genre_scores_gemma":[0.9673381,0.000002714992,0.02631106,0.00007657767,0.0001162694,0.004411097,0.0000482884,0.0001085597,0.001587359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9180737,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660141769001441,"score_gpt":0.2461511244726357,"score_spread":0.2295497067826213,"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."}}