{"id":"W2780225074","doi":"10.1016/j.energy.2017.10.125","title":"Multi-objective optimization framework for the selection of configuration and equipment sizing of solar thermal combisystems","year":2017,"lang":"en","type":"article","venue":"Energy","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Sizing; Particle swarm optimization; Thermal energy storage; Selection (genetic algorithm); Process engineering; Solar thermal collector; Solar energy; Engineering; Mathematical optimization; Thermal; Computer science; Mathematics; Electrical engineering; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001286015,0.001558086,0.0013332,0.0009210329,0.0004314182,0.001114688,0.001174252,0.0009558809,0.00390048],"category_scores_gemma":[0.001224846,0.0006780008,0.0009852246,0.0007843045,0.0003693221,0.0007777754,0.0008480565,0.0009518453,0.000420651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007927396,"about_ca_system_score_gemma":0.00130042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004763353,"about_ca_topic_score_gemma":0.006951022,"domain_scores_codex":[0.9996164,0.0001300265,0.00001600309,0.00005351254,0.0001366438,0.00004738064],"domain_scores_gemma":[0.9996872,0.0001568687,0.00004207834,0.00001599491,0.00007780538,0.00002004789],"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":[0.00001764342,0.00003688486,0.0001079287,0.00005194118,0.00002588426,0.00001894929,0.00000978562,0.9820274,0.0009816565,0.001931528,0.0003727927,0.01441758],"study_design_scores_gemma":[0.000004193578,0.00001943783,0.00004929982,0.00000390605,0.000005253704,0.000004222279,0.000003461704,0.9990125,0.0002089777,0.0004998159,0.0001868142,0.000002017335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01371176,0.0003786242,0.9809604,0.00008606556,0.00003139735,0.0000861844,0.0001156848,0.0002272819,0.004402654],"genre_scores_gemma":[0.5779395,0.0004223185,0.4144968,0.0001147209,0.0000559369,0.0004845335,0.0003403642,0.000163698,0.005982172],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004763353,"threshold_uncertainty_score":0.01304835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448633317563219,"score_gpt":0.2347438930798408,"score_spread":0.2202575599042086,"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."}}