{"id":"W3125531171","doi":"10.3390/en14030608","title":"A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings","year":2021,"lang":"en","type":"review","venue":"Energies","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Computer science; Artificial intelligence; Energy (signal processing); Feature (linguistics); Energy modeling; Energy consumption; Data science; Machine learning; Risk analysis (engineering); Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002300352,0.0003171693,0.001323221,0.0002632421,0.00003834705,0.00003038722,0.0001861188,0.0002528891,0.00001260334],"category_scores_gemma":[0.0002250837,0.0003079745,0.0003540745,0.0005199422,0.00001851504,0.0001518651,0.00006492512,0.0002157983,6.380563e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000830424,"about_ca_system_score_gemma":0.00003729126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000366967,"about_ca_topic_score_gemma":0.00003016251,"domain_scores_codex":[0.9986973,0.00006361736,0.0006711533,0.0002391853,0.00009494228,0.0002337881],"domain_scores_gemma":[0.9991741,0.0003043604,0.0002061983,0.0002159292,0.00007321959,0.00002624477],"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":[4.275625e-7,0.000005540471,5.094829e-7,0.06507562,0.00005046937,0.000002446814,0.000009190016,0.1260814,0.000001044477,0.001801672,0.0002317409,0.8067399],"study_design_scores_gemma":[0.00002987618,0.00001242863,1.429199e-8,0.1547423,0.0001261232,0.00001203694,0.00000373597,0.005743207,0.000113652,0.00001675914,0.8389508,0.0002491639],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00000224311,0.980816,0.01847709,0.000001338521,0.0001216394,0.000153436,0.000005116665,0.0002344763,0.0001886508],"genre_scores_gemma":[0.000005717384,0.9672284,0.03174983,0.00002342136,0.00007920453,0.0004497561,0.0002485495,0.0001043417,0.0001107877],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.838719,"threshold_uncertainty_score":0.9999372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03817094283733553,"score_gpt":0.2730343484977007,"score_spread":0.2348634056603652,"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."}}