{"id":"W4405601974","doi":"10.1109/synergymed62435.2024.10799384","title":"The nexus between design and control: a data-driven approach for leveraging flexibility potential of micro-grids","year":2024,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Concordia University","funders":"Hydro-Québec","keywords":"Nexus (standard); Flexibility (engineering); Computer science; Control (management); Distributed computing; Industrial engineering; Systems engineering; Embedded system; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001084996,0.0007465478,0.0005303634,0.0005192957,0.0003835548,0.001834378,0.001048607,0.0006558598,0.001771358],"category_scores_gemma":[0.002098503,0.0004867291,0.0004696249,0.0005112669,0.0008887995,0.001136588,0.0008056479,0.0009922354,0.0002354885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832912,"about_ca_system_score_gemma":0.002590133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02352485,"about_ca_topic_score_gemma":0.04186171,"domain_scores_codex":[0.9994801,0.000169838,0.00002065965,0.0001013401,0.0001839738,0.00004416071],"domain_scores_gemma":[0.9991191,0.0004363443,0.0001132135,0.0001291258,0.0001634027,0.00003869727],"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":[0.00003033439,0.00003775875,0.001123198,0.00008087328,0.00002581163,0.00004834371,0.00006539057,0.9452188,0.001289424,0.0168507,0.0004831062,0.03474626],"study_design_scores_gemma":[0.000004038344,0.0000175223,0.0002554337,0.00001590923,0.000005462524,0.000007547837,0.00003304377,0.9888833,0.0005653861,0.008634253,0.001571581,0.000006605874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02432536,0.0003689145,0.9656974,0.0009450057,0.00003599676,0.00009363251,0.0002085317,0.0003717728,0.007953407],"genre_scores_gemma":[0.7593053,0.0005462781,0.2357218,0.000173644,0.00005003546,0.0002241064,0.0002606546,0.0001180888,0.003600093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02352485,"threshold_uncertainty_score":0.04677582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0471630452214629,"score_gpt":0.2509678185265157,"score_spread":0.2038047733050528,"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."}}