{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001740219,0.0005128247,0.00142187,0.0002967567,0.00006178571,0.0001088524,0.0002799753,0.0003401127,0.00002004671],"category_scores_gemma":[0.0000447292,0.00048494,0.0003312657,0.0007750872,0.00002350709,0.0003424961,0.00009768472,0.000370135,9.10572e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001672466,"about_ca_system_score_gemma":0.00004317417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001205361,"about_ca_topic_score_gemma":0.00006386877,"domain_scores_codex":[0.9981495,0.00008898637,0.0007897721,0.0003659191,0.0001564667,0.0004494042],"domain_scores_gemma":[0.9991804,0.0001698791,0.0001925793,0.0003804996,0.00002680524,0.00004980718],"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":[5.805257e-7,0.000003757349,6.189487e-7,0.006291916,0.00002860801,0.000008159879,0.000002297666,0.660015,1.019546e-7,0.000735942,0.0001548646,0.3327581],"study_design_scores_gemma":[0.00002770552,0.000005178253,3.18987e-8,0.02767558,0.0001917507,0.00003008495,0.000001016923,0.5991521,8.320183e-7,0.00001420835,0.3725414,0.0003600219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006593277,0.9935597,0.004853939,0.000001714272,0.001015169,0.0001710793,0.000005275224,0.000238456,0.00008879567],"genre_scores_gemma":[0.0001221453,0.9972687,0.001776722,0.00005530646,0.0003473792,0.0000727248,0.0001324006,0.0001564079,0.00006826675],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3723865,"threshold_uncertainty_score":0.9997602,"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."}}