{"id":"W2906865296","doi":"10.3390/en12010149","title":"Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"United Arab Emirates University","keywords":"Computer science; Deep learning; Artificial intelligence; Autocorrelation; Benchmark (surveying); Recurrent neural network; Artificial neural network; Time series; Boosting (machine learning); Autoregressive integrated moving average; Machine learning; Sequence (biology); Autoregressive model; Sequence learning; Term (time); Econometrics; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371555,0.0001765069,0.0001882872,0.00007795545,0.00009933443,0.00007000253,0.00007293726,0.00008136976,0.000005140488],"category_scores_gemma":[0.00005277148,0.0001738015,0.00003109555,0.00009805378,0.00002514105,0.0002839481,0.00004183544,0.0001376881,9.193018e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004299714,"about_ca_system_score_gemma":0.00001120966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001260769,"about_ca_topic_score_gemma":0.00003424295,"domain_scores_codex":[0.9991629,0.00001044757,0.000167045,0.0002201425,0.0001039408,0.0003355743],"domain_scores_gemma":[0.999625,0.0001586914,0.00002413911,0.00009142265,0.00003681454,0.00006398059],"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":[0.000008783287,0.000006743454,0.002676047,0.0001570108,0.00003011197,0.00000358272,0.0009308185,0.8290879,0.1060872,0.0002993698,0.000009836315,0.06070266],"study_design_scores_gemma":[0.0002652811,0.00007725324,0.0002319988,0.00007895486,0.00001376359,0.00002778062,0.0001000013,0.9789737,0.01909534,0.0001505056,0.0007443011,0.0002411502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829583,0.004355561,0.01066725,0.000007025747,0.0002203572,0.0001018262,0.000001776256,0.0002485921,0.00143937],"genre_scores_gemma":[0.9913027,0.0002256996,0.008001764,0.00001509867,0.00009205847,0.00002268008,0.000007893364,0.00004669552,0.0002853549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1498858,"threshold_uncertainty_score":0.7087421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03839637829722571,"score_gpt":0.2242253075215823,"score_spread":0.1858289292243566,"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."}}