{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002249097,0.0002740839,0.00024931,0.0003725874,0.000137877,0.0003449064,0.0002978656,0.0003396858,0.001048746],"category_scores_gemma":[0.0005692564,0.0001611306,0.0002134392,0.000553055,0.00009304003,0.0002895073,0.0001117277,0.000313389,0.0002486392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005073787,"about_ca_system_score_gemma":0.0002900071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02567171,"about_ca_topic_score_gemma":0.02713568,"domain_scores_codex":[0.9999206,0.0000207359,0.000004259624,0.00001661153,0.00002556228,0.00001229498],"domain_scores_gemma":[0.9998531,0.00007176666,0.00001783953,0.0000101966,0.00004086237,0.000006102703],"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":[0.00004150347,0.00003445552,0.006295722,0.00002472402,0.00001530012,0.00006413291,0.00001977539,0.9721953,0.002469596,0.0008287635,0.0006748918,0.01733578],"study_design_scores_gemma":[6.705395e-7,0.000004216248,0.001187911,9.089961e-7,0.000001020364,0.000002797967,0.000005521083,0.9982259,0.0002318329,0.0001806133,0.0001572105,0.000001389511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8601262,0.0003161845,0.129583,0.0003791472,0.00007076465,0.00004046633,0.001118432,0.0004847777,0.007881039],"genre_scores_gemma":[0.9927295,0.00008575729,0.005250386,0.000007028808,0.000006761331,0.000012641,0.0002732448,0.000007368741,0.001627338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02567171,"threshold_uncertainty_score":0.05104458,"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."}}