{"id":"W2395647033","doi":"10.1109/waina.2016.160","title":"Cost and Load Reduction Using Heuristic Algorithms in Smart Grid","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Computer science; Smart grid; Particle swarm optimization; Demand response; Genetic algorithm; Mathematical optimization; Reduction (mathematics); Heuristics; Heuristic; Energy consumption; Schedule; Electricity; Scheduling (production processes); Algorithm; Engineering; Artificial intelligence; Mathematics","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":[0.00009919192,0.00008197605,0.000076968,0.00007648532,0.0000158559,0.00001425189,0.00003674089,0.0000284379,0.00006120675],"category_scores_gemma":[0.0000126683,0.00006384902,0.00001023457,0.00009672654,0.00001925636,0.0001150021,0.00002854243,0.00003348046,0.0000247598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002008485,"about_ca_system_score_gemma":0.000005433453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001352098,"about_ca_topic_score_gemma":0.00005358054,"domain_scores_codex":[0.9995056,0.000008477412,0.0001160579,0.0001234271,0.00008520595,0.0001612779],"domain_scores_gemma":[0.9998126,0.0000145779,0.000007817477,0.0001151782,0.00001135318,0.00003845031],"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":[0.00004545645,0.0001224239,0.01869317,0.000271643,0.0002118828,0.0001216976,0.0005532244,0.4794985,0.08243244,0.003376449,0.03703607,0.377637],"study_design_scores_gemma":[0.003317199,0.00007263664,0.06891526,0.0003652476,0.00006079182,0.0001345033,0.0002797306,0.7752082,0.02522679,0.0007687747,0.1244045,0.001246312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8029628,0.0002735422,0.1788072,0.0002876094,0.003605276,0.0002790072,0.0000041846,0.0005020692,0.01327826],"genre_scores_gemma":[0.9947399,0.0001533448,0.004129195,0.00001560365,0.0002822861,0.00001422457,0.000001231997,0.00002491958,0.000639238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3763907,"threshold_uncertainty_score":0.2603688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411638318773154,"score_gpt":0.2291826787525061,"score_spread":0.2050662955647746,"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."}}