{"id":"W3216423019","doi":"10.3389/fenrg.2021.720406","title":"Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach","year":2021,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Forskningsråd; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Computer science; Residual; Convolutional neural network; Scalability; Time series; Mean squared error; Multilayer perceptron; Artificial intelligence; Perceptron; Deep learning; Artificial neural network; Pattern recognition (psychology); Algorithm; Machine learning; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003083013,0.0007801162,0.0008521248,0.0004043886,0.0001878218,0.0006443165,0.0007191662,0.0005968846,0.001420154],"category_scores_gemma":[0.000528974,0.0003606284,0.0008421291,0.0004219555,0.0001903217,0.0007287462,0.0004007616,0.0007329325,0.0003106102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004299511,"about_ca_system_score_gemma":0.0005668376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02319372,"about_ca_topic_score_gemma":0.01833534,"domain_scores_codex":[0.9998527,0.00002220889,0.000009972293,0.00005556172,0.00003388593,0.00002573877],"domain_scores_gemma":[0.9998517,0.00006175847,0.00002174764,0.00001216285,0.00004400956,0.000008676556],"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":[0.00004698053,0.00003313472,0.0007347685,0.00002207516,0.0000436036,0.00005510945,0.00001332984,0.9743585,0.001700504,0.0006442554,0.0005141051,0.02183362],"study_design_scores_gemma":[7.04027e-7,0.000003347494,0.00005869539,3.888637e-7,0.000001466261,0.000001223547,6.706504e-7,0.9997497,0.00007317289,0.00008421931,0.00002530889,9.956477e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.230449,0.001307155,0.7564937,0.0005054613,0.0001876741,0.00006105449,0.0006581063,0.003780957,0.006556829],"genre_scores_gemma":[0.9683866,0.0002923153,0.0267994,0.00008460331,0.00005122659,0.00005715687,0.0006029213,0.00006624057,0.003659577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02319372,"threshold_uncertainty_score":0.04611742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03351865259392544,"score_gpt":0.2506167934675062,"score_spread":0.2170981408735808,"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."}}