{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002565588,0.001164034,0.001106353,0.001162147,0.0004814037,0.00122934,0.001287797,0.001585152,0.001250287],"category_scores_gemma":[0.004100543,0.0004155124,0.0006442149,0.00113626,0.0006079438,0.0009713119,0.0008231096,0.001232741,0.0001455248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577626,"about_ca_system_score_gemma":0.00164583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415218,"about_ca_topic_score_gemma":0.01088361,"domain_scores_codex":[0.9989917,0.0005493892,0.00004334634,0.0001094817,0.0002201646,0.0000859196],"domain_scores_gemma":[0.9983944,0.00110946,0.0001338236,0.00003419891,0.0002803839,0.00004771344],"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":[0.00001262114,0.00001920099,0.0002937214,0.000009563053,0.00001167918,0.00001343955,0.000003364251,0.9957134,0.00006445579,0.001023925,0.00006576807,0.002768979],"study_design_scores_gemma":[0.000001471248,0.000005330629,0.00003272143,0.000002014407,0.000001335594,0.000001083465,0.000001123975,0.9994699,0.00002656783,0.000426644,0.00003041906,0.000001334045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1048449,0.0008106387,0.8857398,0.0006965965,0.00008055704,0.0001487477,0.0003835084,0.0002965078,0.006998717],"genre_scores_gemma":[0.920395,0.0003508498,0.07670101,0.0001012313,0.00004783104,0.0002891075,0.0003128948,0.0000205194,0.001781647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01415218,"threshold_uncertainty_score":0.02813959,"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."}}