{"id":"W3200609747","doi":"10.17762/de.vi.4248","title":"Load Forecasting using Time Series Techniques","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Time series; Series (stratigraphy); Exponential smoothing; Computer science; Electric power system; Probabilistic forecasting; Moving average; Electrical load; Data mining; Power (physics); Econometrics; Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004767187,0.0004616268,0.0003886475,0.001549375,0.0002075248,0.00081356,0.0003784656,0.000468289,0.00174163],"category_scores_gemma":[0.001873921,0.0001299577,0.0005135341,0.002240623,0.0001306102,0.001332108,0.0002562709,0.0005236312,0.0007698848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002689896,"about_ca_system_score_gemma":0.0002278023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003986689,"about_ca_topic_score_gemma":0.002510267,"domain_scores_codex":[0.9996712,0.00007640645,0.00002413724,0.00005967077,0.0001474195,0.00002108188],"domain_scores_gemma":[0.9995921,0.0001817985,0.00006965577,0.00004838978,0.00009812635,0.000009909181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001091423,0.0001145986,0.0128578,0.0002788849,0.0001917388,0.0002985818,0.0001914685,0.3872164,0.009543618,0.01920657,0.006173189,0.563818],"study_design_scores_gemma":[0.000006242851,0.00006089678,0.004194035,0.00004372327,0.0000310655,0.0001275925,0.00006281566,0.9779481,0.002003178,0.008346682,0.007149766,0.00002590464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08774425,0.00285794,0.8877579,0.0006564041,0.0003620112,0.00009431593,0.0009171448,0.002194787,0.01741529],"genre_scores_gemma":[0.8619514,0.004472392,0.1251713,0.0001047287,0.0002829036,0.0001033736,0.001214611,0.0001028976,0.006596424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003986689,"threshold_uncertainty_score":0.007926941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360214618304822,"score_gpt":0.1948858072523147,"score_spread":0.1712836610692665,"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."}}