{"id":"W3034174159","doi":"10.1002/er.5537","title":"Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for <scp>HVAC</scp> control","year":2020,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"HVAC; Artificial neural network; Mean squared error; Controller (irrigation); Engineering; Air conditioning; Automotive engineering; Computer science; Simulation; Algorithm; Real-time computing; Machine learning; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005043966,0.00007883505,0.000150353,0.0001216069,0.00008047838,0.00004320244,0.0002082462,0.00007653022,0.000003185658],"category_scores_gemma":[0.0001281935,0.00006261685,0.0000658057,0.0001685125,0.00005870854,0.0001303367,0.00002714161,0.0001597513,1.359895e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000479318,"about_ca_system_score_gemma":0.00005565028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002571939,"about_ca_topic_score_gemma":0.000002350918,"domain_scores_codex":[0.9988644,0.0000324117,0.000441933,0.00008858224,0.0004243492,0.000148329],"domain_scores_gemma":[0.9984136,0.0004994691,0.0001152004,0.00004761115,0.0008697818,0.00005433886],"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":[0.0001423146,0.00001686177,0.00003395997,0.00001775293,0.0001900481,0.000001273688,0.00012082,0.9799026,0.004068127,0.00399912,0.001041554,0.01046554],"study_design_scores_gemma":[0.0006087724,0.0001246213,0.00004297583,0.00004842864,0.00001212317,0.00001089343,0.0001026873,0.9744282,0.01848459,0.0002020048,0.005910009,0.00002468989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02969843,0.0007301164,0.9686092,0.0002549236,0.0005562514,0.0001027071,0.00002336422,0.00001023571,0.00001476176],"genre_scores_gemma":[0.9559041,0.0002722901,0.04263273,0.00002856213,0.001084801,0.00002150965,0.00002217668,0.00002004597,0.00001378974],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9262057,"threshold_uncertainty_score":0.2553441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04477958646804928,"score_gpt":0.2854098240622662,"score_spread":0.240630237594217,"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."}}