{"id":"W4312178129","doi":"10.18280/ria.360505","title":"Diffusion Convolutional Recurrent Neural Network-Based Load Forecasting During COVID-19 Pandemic Situation","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Term (time); Normalization (sociology); Computer science; Coronavirus disease 2019 (COVID-19); Recurrent neural network; Artificial neural network; Statistics; Artificial intelligence; Econometrics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0004329642,0.0007081341,0.0004886099,0.0005163015,0.0002183601,0.0005188185,0.0007036545,0.0004352713,0.0008472715],"category_scores_gemma":[0.001302031,0.000231521,0.0004982031,0.0003816969,0.0001583874,0.0006459859,0.0003900796,0.0007265214,0.0002608021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007148628,"about_ca_system_score_gemma":0.0007110111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03929055,"about_ca_topic_score_gemma":0.0328919,"domain_scores_codex":[0.9998279,0.00002221423,0.00001433773,0.00005803564,0.00003693761,0.0000406017],"domain_scores_gemma":[0.9997559,0.00007231299,0.00003110267,0.00001722751,0.0001023948,0.00002104823],"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":[0.0003943157,0.0001758095,0.02207241,0.0001084034,0.0001529536,0.000310843,0.0001237511,0.8367348,0.006710556,0.001192578,0.006203155,0.1258205],"study_design_scores_gemma":[0.000002710424,0.00001180913,0.001217991,0.000002913745,0.000008039495,0.000006719684,0.000007658728,0.9979247,0.0005382795,0.0001682706,0.000106801,0.000004125263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.869047,0.00185325,0.1144041,0.001253575,0.0004048923,0.00005755778,0.001258322,0.002342968,0.009378324],"genre_scores_gemma":[0.9914268,0.0002362981,0.005398386,0.00006879453,0.00003030068,0.00001660143,0.0008050435,0.0000232208,0.001994686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03929055,"threshold_uncertainty_score":0.07812369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06182576053777536,"score_gpt":0.2605008735406535,"score_spread":0.1986751130028781,"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."}}