{"id":"W4285822645","doi":"10.26868/25222708.2021.30290","title":"Coupling of neural models for predicting indoor temperatures and heating loads in buildings","year":2021,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thermostat; HVAC; Mean squared error; Building energy simulation; Environmental science; Coupling (piping); Artificial neural network; Energy (signal processing); Work (physics); Computer science; Cooling load; Meteorology; Simulation; Engineering; Energy performance; Statistics; Mechanical engineering; Mathematics; Machine learning; Air conditioning; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005902709,0.0008247115,0.0005281634,0.0003806944,0.0002803761,0.0006280781,0.0007997867,0.0008255816,0.001116407],"category_scores_gemma":[0.00170686,0.0005283769,0.0005405436,0.0003341514,0.0004904657,0.0007612674,0.0007834171,0.0007400446,0.0001664087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009059768,"about_ca_system_score_gemma":0.0005987735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02334291,"about_ca_topic_score_gemma":0.01473669,"domain_scores_codex":[0.9997718,0.00006877236,0.00001332898,0.00005247406,0.00005516494,0.00003848696],"domain_scores_gemma":[0.9994988,0.0003057845,0.00005374759,0.00004087923,0.00007842596,0.00002229877],"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":[0.00001068286,0.0000120453,0.0003046857,0.000004520849,0.000007658467,0.000004676815,0.000005714873,0.9979144,0.0001933976,0.00007994474,0.00001710124,0.001445187],"study_design_scores_gemma":[0.000001101542,0.000006841596,0.0001097493,6.932776e-7,0.000001582893,7.927014e-7,0.000001586919,0.9996319,0.0001361276,0.00009037503,0.00001809634,0.000001161877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6988204,0.000366904,0.2918119,0.0001542235,0.0000739148,0.00008331438,0.0001668279,0.0007831265,0.007739408],"genre_scores_gemma":[0.9908094,0.0000635868,0.007799638,0.00001956997,0.000006996333,0.00004596543,0.00009488783,0.00002103123,0.001138994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02334291,"threshold_uncertainty_score":0.04641408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008435837511492,"score_gpt":0.2494661241947831,"score_spread":0.2293817658196682,"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."}}