{"id":"W3116200246","doi":"10.1109/pesgm41954.2020.9282124","title":"Forecasting Electric Load by Aggregating Meteorological and History-based Deep Learning Modules","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Computer science; Electric power system; Electrical load; Artificial neural network; Metric (unit); Probabilistic logic; Probabilistic forecasting; Power (physics); Simulation; Real-time computing; Artificial intelligence; Statistics; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002231526,0.0007434333,0.0003239728,0.0005350436,0.000172503,0.0003866966,0.0005544323,0.0003609446,0.001097988],"category_scores_gemma":[0.0006278738,0.0003273757,0.0003061622,0.0006830612,0.0001395474,0.0008879403,0.0004194969,0.000645929,0.0004751912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004408529,"about_ca_system_score_gemma":0.0004451492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01509241,"about_ca_topic_score_gemma":0.02483342,"domain_scores_codex":[0.9999231,0.000007754587,0.000005520358,0.00002709339,0.00001930322,0.00001725021],"domain_scores_gemma":[0.9998519,0.0000323473,0.00002254627,0.00002303784,0.00005806372,0.00001212736],"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":[0.0001366474,0.0002218012,0.01229818,0.00003502491,0.00009100543,0.00008192164,0.00004484349,0.7604805,0.005849746,0.0008612669,0.003677383,0.2162216],"study_design_scores_gemma":[0.000002113366,0.000009651922,0.001560218,0.000002201444,0.000006412457,0.000004211517,0.000003679949,0.9971029,0.0006786612,0.0004071373,0.0002194075,0.000003418288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5287806,0.0007529788,0.4535291,0.0006370004,0.0002884912,0.00005689838,0.001906271,0.00438334,0.009665287],"genre_scores_gemma":[0.9792022,0.0001826894,0.01695573,0.00005809614,0.00004931726,0.00002080434,0.0009419692,0.00002945121,0.002559769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01509241,"threshold_uncertainty_score":0.03000909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01930385540824122,"score_gpt":0.1833269469183894,"score_spread":0.1640230915101481,"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."}}