{"id":"W4308198113","doi":"10.3390/su142114385","title":"Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Benchmark (surveying); Algorithm; Energy demand; Fuzzy logic; Particle swarm optimization; Inference system; Metaheuristic; Computer science; Fuzzy inference system; Genetic algorithm; Mathematical optimization; Mathematics; Machine learning; Artificial intelligence; Fuzzy control system","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":[0.0003322465,0.0006333744,0.0004788387,0.0004776751,0.000231823,0.0006412997,0.0005048164,0.0007462526,0.0009142124],"category_scores_gemma":[0.0007709931,0.0003112416,0.0006445991,0.0003379097,0.000221112,0.000407204,0.0002398618,0.0004672207,0.0001758224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004967963,"about_ca_system_score_gemma":0.000488459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023324,"about_ca_topic_score_gemma":0.009851921,"domain_scores_codex":[0.9999022,0.00002683885,0.000006580041,0.00002348717,0.00002639701,0.00001446981],"domain_scores_gemma":[0.9997552,0.0001424803,0.00003571484,0.00001376388,0.00004590961,0.000006856654],"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":[0.0000135348,0.00001178677,0.0002594187,0.00001411045,0.00001126066,0.000009255124,0.000009142118,0.9928207,0.0006839716,0.0002629999,0.00004717518,0.005856644],"study_design_scores_gemma":[0.000001152597,0.00000553271,0.00006375898,0.000001913062,0.000001805685,0.000001434491,0.000002182262,0.9995822,0.00018738,0.000111272,0.00004026862,0.000001050403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2185936,0.0004373482,0.7720459,0.0001510426,0.00005434777,0.00007470182,0.000213725,0.0004651824,0.007964186],"genre_scores_gemma":[0.9621796,0.0001149218,0.03638433,0.00002280121,0.000008490235,0.00007974397,0.0001045475,0.00001399085,0.001091592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023324,"threshold_uncertainty_score":0.0203473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125934795859804,"score_gpt":0.1949452073834271,"score_spread":0.1823517277974467,"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."}}