{"id":"W4387821028","doi":"10.9734/ajeba/2023/v23i211127","title":"Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model","year":2023,"lang":"en","type":"article","venue":"Asian Journal of Economics Business and Accounting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Energy consumption; Consumption (sociology); Electricity; Time series; Computer science; Econometrics; Energy (signal processing); HVAC; Regression analysis; Energy accounting; Environmental economics; Environmental science; Engineering; Economics; Statistics; Mathematics; Machine learning; Air conditioning","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":[],"consensus_categories":[],"category_scores_codex":[0.0003395007,0.0001318169,0.0002743358,0.0002919937,0.00007620676,0.00006191724,0.00009239444,0.00006474076,0.000005414106],"category_scores_gemma":[0.00008382263,0.000120448,0.00007107505,0.0001530478,0.00002302939,0.0003880706,0.00001874574,0.0001151412,8.935299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003005186,"about_ca_system_score_gemma":0.00003221545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.680359e-7,"about_ca_topic_score_gemma":0.000004610273,"domain_scores_codex":[0.9992692,0.000005630204,0.0004186589,0.0000945438,0.00003132749,0.0001806196],"domain_scores_gemma":[0.9994002,0.00007929193,0.0002932023,0.00006806285,0.0001211107,0.00003811901],"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":[0.0001787832,0.00001920448,0.001272603,0.0004007091,0.0000488545,0.000004391626,0.0001214637,0.9653287,0.008010803,0.0004620072,0.0003255905,0.02382689],"study_design_scores_gemma":[0.0004641729,0.00004628891,0.0004263152,0.0006396818,0.00001932777,0.00002074339,0.00002662356,0.9929964,0.004437869,0.0004592353,0.000340853,0.0001224882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894584,0.00009196364,0.009846886,0.0001507991,0.0002028034,0.00005592885,0.00002309751,0.00002787834,0.0001422601],"genre_scores_gemma":[0.9917768,0.0001368599,0.007805842,0.00002354549,0.0001963924,0.000002109242,0.00001071893,0.00003367586,0.00001408113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0276677,"threshold_uncertainty_score":0.4911726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03127661447383213,"score_gpt":0.2281407496782002,"score_spread":0.1968641352043681,"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."}}