{"id":"W2985287323","doi":"10.1002/gch2.201900065","title":"Modeling and Forecasting of Energy Demands for Household Applications","year":2019,"lang":"en","type":"article","venue":"Global Challenges","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Autoregressive integrated moving average; Autoregressive model; Energy (signal processing); Energy consumption; Artificial neural network; Spline interpolation; Solar energy; Interpolation (computer graphics); Environmental science; Moving average; Econometrics; Computer science; Meteorology; Time series; Statistics; Engineering; Mathematics; Geography; Telecommunications; Artificial intelligence; Electrical engineering","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":[0.00007146915,0.0001019812,0.0001558234,0.00002545881,0.00002785215,0.00000771441,0.00007285291,0.00006728037,0.000001242797],"category_scores_gemma":[0.000005335075,0.0001040706,0.00004033329,0.00005235631,0.000009915059,0.00005906346,0.00002444918,0.00003108225,4.581433e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000163425,"about_ca_system_score_gemma":0.000004997485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159063,"about_ca_topic_score_gemma":0.00005056592,"domain_scores_codex":[0.999458,0.000003896253,0.0001678063,0.0001357282,0.00006352807,0.0001711071],"domain_scores_gemma":[0.9997469,0.0000416905,0.00002290183,0.0001202379,0.00002630895,0.00004200875],"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":[0.00001013861,0.00001268924,0.0004081492,0.0005613411,0.00005675054,2.582697e-7,0.0001035146,0.7988786,0.0003196477,0.1008611,0.00001631169,0.09877144],"study_design_scores_gemma":[0.0003937942,0.00004937372,0.00009571416,0.00009689554,0.00001728777,0.000009413373,0.0001788062,0.9884468,0.0004622132,0.004658103,0.005382035,0.0002095897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7479702,0.04345803,0.1568907,0.00007114629,0.0003352528,0.0003053626,0.0001296346,0.0004040085,0.05043564],"genre_scores_gemma":[0.9961189,0.001309561,0.002390346,0.000007927496,0.00008860372,0.00004412505,0.000008204353,0.00001780255,0.00001454926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2481487,"threshold_uncertainty_score":0.4243876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03977800290628745,"score_gpt":0.2201153764851382,"score_spread":0.1803373735788508,"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."}}