{"id":"W4200256026","doi":"10.18280/ejee.230602","title":"Forecasting of Electricity Demand by Hybrid ANN-PSO under Shadow of the COVID-19 Pandemic","year":2021,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electricity; Coronavirus disease 2019 (COVID-19); Term (time); Computer science; Pandemic; Shadow (psychology); Variable (mathematics); Consumption (sociology); Econometrics; Engineering; Economics; Mathematics; Medicine; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008503303,0.0002140726,0.0004047697,0.0001587821,0.0000582412,0.0000214649,0.0003606608,0.00003943887,0.0000235222],"category_scores_gemma":[0.001274482,0.0001754413,0.0002425174,0.0007918485,0.00002850285,0.000108376,0.00005857005,0.0006593181,6.439419e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001454871,"about_ca_system_score_gemma":0.0001179784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003834866,"about_ca_topic_score_gemma":0.000001292755,"domain_scores_codex":[0.9981647,0.0001591292,0.0008256456,0.0001365448,0.0003329049,0.0003810333],"domain_scores_gemma":[0.9987603,0.0003948405,0.0002736561,0.0001835338,0.0001325773,0.0002550599],"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":[0.00001723383,0.00003117265,0.00115003,0.0001322675,0.0001417595,0.0001093987,0.00009716133,0.8911441,0.1026542,0.000132677,0.001166011,0.003223986],"study_design_scores_gemma":[0.002204427,0.0004091925,0.001288045,0.0004362901,0.0002058407,0.004823808,0.00003483428,0.7629765,0.2143812,0.0002052409,0.01232873,0.0007058879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6854984,0.008441397,0.3036056,0.00009152354,0.0004650016,0.00007035466,0.00001320589,0.00008754882,0.001726967],"genre_scores_gemma":[0.9984069,0.0002391268,0.0009928127,0.00009122677,0.000164553,4.006889e-7,0.00000234257,0.00006221265,0.00004045821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3129085,"threshold_uncertainty_score":0.7154289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219410416452954,"score_gpt":0.2090902458767937,"score_spread":0.1868961417122642,"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."}}