{"id":"W4282943189","doi":"10.3390/buildings12060829","title":"Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework","year":2022,"lang":"en","type":"article","venue":"Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial neural network; Energy consumption; Genetic algorithm; Process (computing); Multi-objective optimization; Range (aeronautics); Efficient energy use; Decision support system; Computer science; Energy (signal processing); Greenhouse gas; Hyperparameter; Engineering; Pareto principle; Reliability engineering; Machine learning; Artificial intelligence; Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005108217,0.0003279033,0.0003298282,0.0002061979,0.0009649802,0.0001010751,0.0004358944,0.0001830741,0.0001687023],"category_scores_gemma":[0.00006094347,0.000384065,0.0001743372,0.000776742,0.00003060878,0.0002946961,0.0002228788,0.0003740204,5.936157e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002077181,"about_ca_system_score_gemma":0.00003928359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003618287,"about_ca_topic_score_gemma":0.000009358953,"domain_scores_codex":[0.9977074,0.00003781778,0.0005627895,0.0004574475,0.0004452368,0.0007893346],"domain_scores_gemma":[0.9990495,0.0002776651,0.0001364253,0.0003369029,0.00006552655,0.0001340038],"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":[0.0001224209,0.0000184622,0.001114456,0.0000215123,0.00003999365,0.000004466166,0.00007864828,0.913528,0.0004790539,0.02358586,0.004782886,0.05622428],"study_design_scores_gemma":[0.0002532228,0.0002041073,0.00009382649,0.00005857646,0.00003913241,0.00003637376,0.00003301652,0.9331658,0.003411357,0.005528158,0.05673703,0.0004393704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5075939,0.000283974,0.4883439,0.00004796036,0.002658483,0.0001074705,0.00002378544,0.0007211012,0.0002193677],"genre_scores_gemma":[0.944712,0.0001005081,0.05313357,0.0003188874,0.001042517,0.0003163136,0.000106144,0.0001351359,0.0001349361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4371181,"threshold_uncertainty_score":0.9998611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009873462444574335,"score_gpt":0.2141907669430897,"score_spread":0.2043173044985153,"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."}}