{"id":"W2969326186","doi":"10.3390/en12173254","title":"Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review","year":2019,"lang":"en","type":"review","venue":"Energies","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":261,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Energy consumption; Variety (cybernetics); Robustness (evolution); Computer science; Software deployment; Sustainability; Artificial intelligence; Engineering; Ecology","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":[0.0006273369,0.0010102,0.0008068109,0.001732502,0.000184957,0.0009270011,0.0008880384,0.0008075247,0.001814324],"category_scores_gemma":[0.001512172,0.0003108659,0.0007404305,0.003135729,0.0002118308,0.001059182,0.0004195377,0.0005592995,0.0006442171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003948654,"about_ca_system_score_gemma":0.0008425849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004107356,"about_ca_topic_score_gemma":0.004049579,"domain_scores_codex":[0.9997463,0.0000434864,0.00004721836,0.00005420792,0.00009604823,0.00001276572],"domain_scores_gemma":[0.9993169,0.0004204938,0.0000726394,0.00001438104,0.0001622201,0.00001325931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004557343,0.00007823057,0.001365344,0.02386726,0.0002164876,0.0001064592,0.00006722495,0.01323518,0.000854768,0.004072157,0.01080838,0.9452831],"study_design_scores_gemma":[0.00002110492,0.0003633557,0.009480985,0.02874475,0.001508975,0.001147039,0.0003837179,0.03118223,0.003919531,0.01086788,0.912174,0.0002062988],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001214049,0.9930255,0.002723515,0.0003585655,0.0002637557,0.00001331714,0.0001148677,0.00002580244,0.002260656],"genre_scores_gemma":[0.006071923,0.9919364,0.001224437,0.00006296766,0.0001567572,0.000009477944,0.0001082637,0.000004286572,0.0004254947],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004107356,"threshold_uncertainty_score":0.008166909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058941403060923,"score_gpt":0.2814431036769987,"score_spread":0.1755489633709064,"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."}}