{"id":"W2909058763","doi":"10.1109/intlec.2018.8612420","title":"A Novel Energy Management Technique for DC Microgrids in Telecom Applications","year":2018,"lang":"en","type":"article","venue":"","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Voltage droop; Backup; Voltage; Reliability (semiconductor); Energy storage; Energy management; State of charge; Computer science; Microgrid; Battery (electricity); Energy management system; Nonlinear system; Control theory (sociology); Energy (signal processing); Engineering; Electrical engineering; Control (management); Voltage regulator; Power (physics)","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":[0.00005013636,0.0003941754,0.0002014097,0.0002745528,0.0003144359,0.000291302,0.0003936578,0.0001921945,0.001300788],"category_scores_gemma":[0.00009443478,0.00008219837,0.0002006143,0.0002669351,0.000162375,0.0003986683,0.000326955,0.0003214473,0.0002819196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416732,"about_ca_system_score_gemma":0.0001472804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007929992,"about_ca_topic_score_gemma":0.001188218,"domain_scores_codex":[0.9999554,0.000005576015,0.000002911266,0.00001319648,0.00001836037,0.000004532797],"domain_scores_gemma":[0.9999726,0.000004031445,0.000004741916,0.000005168171,0.00001079614,0.000002591296],"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":[0.0001475558,0.0001386167,0.0005873949,0.0005222505,0.00005784947,0.0003527383,0.0002565634,0.08889356,0.2958381,0.02994826,0.00433009,0.578927],"study_design_scores_gemma":[0.00004556644,0.0004072302,0.001096731,0.00005502562,0.00006521604,0.0007665923,0.0001136652,0.8695237,0.07379236,0.007844561,0.04624483,0.00004447568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03059128,0.001309463,0.9526107,0.0002254344,0.0001254102,0.0001047978,0.00005116414,0.0005367121,0.01444499],"genre_scores_gemma":[0.8535371,0.001201341,0.1356225,0.0001500521,0.0001139617,0.00009862216,0.00006490848,0.00004665249,0.009164896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001300788,"threshold_uncertainty_score":0.004351556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004103485029608093,"score_gpt":0.1932481411104157,"score_spread":0.1891446560808077,"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."}}