{"id":"W4389600135","doi":"10.48550/arxiv.2312.05073","title":"A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Demand response; Scalability; Computer science; Renewable energy; Distributed computing; Distributed generation; Power (physics); Electricity; Engineering; Electrical engineering","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":[0.0006843138,0.0006682039,0.0008129199,0.000254509,0.0003439745,0.000650557,0.001032852,0.0008641816,0.001303597],"category_scores_gemma":[0.001232495,0.0004270188,0.0004732334,0.0003964382,0.0005341129,0.0006201371,0.000839924,0.001381659,0.0001897599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007145406,"about_ca_system_score_gemma":0.001140804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007443164,"about_ca_topic_score_gemma":0.009453237,"domain_scores_codex":[0.999817,0.00005140435,0.000008582863,0.00005281915,0.00004013918,0.00003015736],"domain_scores_gemma":[0.9995878,0.0002119065,0.0000453527,0.00002977274,0.00009479442,0.00003020594],"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":[0.00002205641,0.00001343238,0.0001469221,0.00001240419,0.00001016479,0.00001204833,0.000009894537,0.9868478,0.0003619464,0.001102461,0.0002965569,0.01116432],"study_design_scores_gemma":[0.000001969125,0.000003309766,0.00001177206,6.234858e-7,8.177952e-7,9.366327e-7,0.000001134867,0.9994463,0.00006111077,0.0004177019,0.00005361949,5.503659e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01381362,0.0001308119,0.9837309,0.0002245306,0.00004493414,0.00001726632,0.00004003151,0.0003521605,0.001645861],"genre_scores_gemma":[0.8670881,0.0001013976,0.128921,0.0001740901,0.00005359016,0.00009725178,0.0001567619,0.00007457676,0.003333222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007443164,"threshold_uncertainty_score":0.01479965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05738411943058143,"score_gpt":0.1891443874395999,"score_spread":0.1317602680090184,"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."}}