{"id":"W3008533347","doi":"10.1109/access.2020.2975738","title":"Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Deep learning; Artificial neural network; Feed forward; Encoder; Machine learning; Sequence learning; Scheduling (production processes); Sequence (biology); Feedforward neural network; Time horizon; Control engineering; Engineering; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005829514,0.0007422207,0.0004985494,0.000291777,0.000210789,0.0004380228,0.0007687408,0.0005830236,0.001774184],"category_scores_gemma":[0.001693856,0.0003017799,0.0004271171,0.0004796455,0.0002063587,0.001202436,0.0004766838,0.001269386,0.0004204084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007066409,"about_ca_system_score_gemma":0.0006989377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01710559,"about_ca_topic_score_gemma":0.02189295,"domain_scores_codex":[0.999821,0.00004368622,0.00001057226,0.00004933364,0.00004300574,0.00003242391],"domain_scores_gemma":[0.9996505,0.0001793279,0.00003241322,0.00003290836,0.000089488,0.00001536702],"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":[0.0001983328,0.0001733928,0.001707973,0.00008801815,0.00008184843,0.0001011943,0.00007091759,0.7007602,0.006116874,0.004551637,0.004850953,0.2812988],"study_design_scores_gemma":[0.000002755902,0.00001481413,0.0001240495,0.000002765976,0.00000537811,0.000004456575,0.000002690557,0.9978733,0.0006331146,0.001113929,0.0002203397,0.000002464332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442116,0.003428703,0.8384401,0.001472534,0.0003564197,0.00007641949,0.0005160023,0.003852869,0.007645342],"genre_scores_gemma":[0.9300501,0.0007496441,0.06376675,0.000296461,0.0001001257,0.0000534152,0.0006254182,0.00008294817,0.004275213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01710559,"threshold_uncertainty_score":0.03401202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0776218189074291,"score_gpt":0.2843502902902904,"score_spread":0.2067284713828613,"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."}}