{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001998013,0.0002487179,0.0002303858,0.00008873208,0.00007093902,0.00007803882,0.0001119223,0.0001108404,0.00007259798],"category_scores_gemma":[0.0001009835,0.0002920635,0.0000720344,0.0003545805,0.00001199589,0.0003141356,0.00004431452,0.0002001304,0.00001804231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520951,"about_ca_system_score_gemma":0.00005027793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004228329,"about_ca_topic_score_gemma":0.000001108117,"domain_scores_codex":[0.9989391,0.00001651894,0.0002497215,0.0001957154,0.0001715393,0.0004273788],"domain_scores_gemma":[0.9995247,0.00008215739,0.00002203238,0.0002068665,0.00007430925,0.00008989708],"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":[0.000002057514,0.000003978306,0.00001672052,0.0000724554,0.00003570732,0.0001236169,0.0001159551,0.6758931,0.3197677,0.0001210852,0.000166431,0.003681196],"study_design_scores_gemma":[0.0000570449,0.00001063941,0.00000610035,0.0001642557,0.00001289598,0.0002541678,0.000008126752,0.6231535,0.3717851,0.0000308913,0.00424612,0.0002711586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04007764,0.00127942,0.9486678,0.00001143754,0.000582354,0.0001198151,0.000006611062,0.00264139,0.006613547],"genre_scores_gemma":[0.4213773,0.00006717774,0.577128,0.00002700664,0.0005861529,0.00002729192,0.00001580174,0.0002234928,0.0005478347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3812996,"threshold_uncertainty_score":0.9999532,"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."}}