{"id":"W4392668139","doi":"10.3384/ecp204285","title":"Exploiting Modelica and the OpenIPSL for University Campus Microgrid Model Development","year":2023,"lang":"en","type":"article","venue":"Linköping electronic conference proceedings","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Opal-Rt Technologies (Canada)","funders":"Office of Energy Efficiency; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; National Science Foundation","keywords":"Modelica; Microgrid; Flexibility (engineering); Computer science; Control engineering; Process (computing); Modeling and simulation; Reliability (semiconductor); Generator (circuit theory); Task (project management); Systems engineering; Reliability engineering; Power (physics); Engineering; Simulation; Programming language; Control (management); Artificial intelligence","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":[0.001142532,0.001332181,0.0007587861,0.001208199,0.0006802441,0.002399246,0.001834178,0.001111302,0.01321412],"category_scores_gemma":[0.003308377,0.0008327664,0.001439077,0.001003473,0.0006088176,0.001384458,0.001501924,0.002333737,0.004390081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008068583,"about_ca_system_score_gemma":0.002083529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009887247,"about_ca_topic_score_gemma":0.009979879,"domain_scores_codex":[0.999512,0.000160315,0.00004069639,0.00006707436,0.0001813452,0.00003857682],"domain_scores_gemma":[0.9986727,0.0006980204,0.0000939423,0.0002469184,0.0002570738,0.00003141381],"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":[0.00006878025,0.00007952979,0.001098741,0.0004748807,0.00008995317,0.0003525699,0.0002472904,0.8762006,0.003129598,0.04152443,0.007167815,0.06956587],"study_design_scores_gemma":[0.00002350622,0.00002380657,0.0001184587,0.00007269021,0.00002305905,0.00006506644,0.00003509545,0.9608287,0.002738682,0.01138351,0.02467017,0.00001720697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00331771,0.0001720286,0.9714653,0.0001557281,0.00005105829,0.00008669006,0.0007689397,0.01249461,0.01148786],"genre_scores_gemma":[0.2590036,0.001428515,0.7153961,0.0002127439,0.00007203491,0.001302604,0.00455217,0.003876542,0.01415563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321412,"threshold_uncertainty_score":0.04420561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110609801603358,"score_gpt":0.1835924348796405,"score_spread":0.172486336863607,"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."}}