{"id":"W2559793074","doi":"10.1109/epec.2016.7771702","title":"Comparison of artificial intelligence techniques for energy consumption estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mean squared error; Multilayer perceptron; Radial basis function; Support vector machine; Mean absolute error; Approximation error; Artificial neural network; Energy consumption; Perceptron; Computer science; Artificial intelligence; Mean absolute percentage error; Root mean square; Function (biology); Pattern recognition (psychology); Statistics; Mathematics; Algorithm; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006711485,0.00004175337,0.00007204655,0.000031362,0.00003989612,0.0000176031,0.0002068032,0.00002487799,0.00001245499],"category_scores_gemma":[0.000009727065,0.0000272069,0.00002534841,0.00008027667,0.00003290569,0.0001454648,0.00003487238,0.00001080414,0.000005674452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008260717,"about_ca_system_score_gemma":0.000007742771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005296145,"about_ca_topic_score_gemma":0.000007450289,"domain_scores_codex":[0.9995351,0.000008419976,0.0001940998,0.0001270325,0.0000609575,0.00007435422],"domain_scores_gemma":[0.9995272,0.000142618,0.00007864022,0.000174751,0.00005687988,0.00001994237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001101775,0.00001621734,0.00002714297,0.00000149326,6.157645e-7,6.492702e-9,0.000005338612,0.00002677395,0.008911115,0.4709554,0.0002736436,0.5197811],"study_design_scores_gemma":[0.000009747557,0.00004377002,0.00004078966,0.00001195839,0.000001364697,3.745732e-7,0.000001500509,0.3537481,0.5601609,0.08481801,0.001120518,0.00004301115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009080327,0.00001155332,0.9977253,0.0009503811,0.00003852344,0.0001018176,0.000001438857,0.000106109,0.0001568163],"genre_scores_gemma":[0.7455245,0.000008413477,0.2543182,0.00003381204,0.00001872886,0.00004789957,9.711845e-7,0.000001638659,0.00004585085],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7446164,"threshold_uncertainty_score":0.1109465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08291016527995292,"score_gpt":0.3691861956408504,"score_spread":0.2862760303608975,"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."}}