{"id":"W4281565149","doi":"10.1109/icpc2t53885.2022.9776757","title":"Short-Term Forecasting in Smart Electric Grid Using N-BEATS","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smart grid; Computer science; Robustness (evolution); Autoregressive integrated moving average; Electric power system; Electricity; Time series; Electricity price forecasting; Python (programming language); Demand response; Wind power; Demand forecasting; Grid; Econometrics; Electricity market; Operations research; Machine learning; Engineering; Power (physics); Economics; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004098976,0.000317001,0.0003542313,0.0006262449,0.0002903376,0.0001318373,0.0006420389,0.0001156321,0.0002948627],"category_scores_gemma":[0.00007989114,0.0003465195,0.00008319893,0.0003901526,0.00005884316,0.0001543846,0.0003800613,0.0009355927,0.000002658292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002742831,"about_ca_system_score_gemma":0.00003713691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001691457,"about_ca_topic_score_gemma":0.00002853404,"domain_scores_codex":[0.9981449,0.00006203692,0.000490597,0.0004508478,0.0003337882,0.0005177806],"domain_scores_gemma":[0.9993945,0.0001578079,0.0001042506,0.0002311944,0.00006723329,0.00004499859],"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":[0.0003589741,0.0004142519,0.06354327,0.000180805,0.0008272102,0.0007748245,0.00201243,0.1691554,0.1283897,0.06848776,0.001439631,0.5644158],"study_design_scores_gemma":[0.0008659716,0.0002165221,0.002384089,0.00008673658,0.00001090351,0.0001623516,0.0008753385,0.9911519,0.0009777562,0.001401793,0.001409287,0.0004573283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831638,0.0006547192,0.002286361,0.0002122663,0.001661255,0.0002021316,0.000064969,0.0008396384,0.01091487],"genre_scores_gemma":[0.9993725,0.0000520718,0.000255779,0.0001033152,0.0000597892,0.00002589575,0.00003061566,0.00003683168,0.00006313966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8219966,"threshold_uncertainty_score":0.9998987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644687276521886,"score_gpt":0.2361803271138438,"score_spread":0.2097334543486249,"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."}}