{"id":"W2027327378","doi":"10.1109/icmlc.2010.5580713","title":"Artificial neural network for load forecasting in smart grid","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"City, University of London","keywords":"Smart grid; Artificial neural network; Computer science; Electric power system; Grid; Key (lock); Power grid; Electric power transmission; Power system simulation; Power (physics); Load balancing (electrical power); Representation (politics); Industrial engineering; Operations research; Artificial intelligence; Engineering; Electrical engineering; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002501432,0.0001318292,0.0001368306,0.00004096558,0.00006269195,0.00003759375,0.00009900486,0.00009156986,0.00007674311],"category_scores_gemma":[0.000067217,0.0001276293,0.00005854396,0.000158952,0.00001551228,0.0001050214,0.00001882814,0.0002541577,0.000008786559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001927396,"about_ca_system_score_gemma":0.00001333235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005560867,"about_ca_topic_score_gemma":0.005082901,"domain_scores_codex":[0.9990909,0.000005196163,0.0002501354,0.0001374282,0.00008050478,0.0004358596],"domain_scores_gemma":[0.999644,0.0001350945,0.00001808779,0.0001171766,0.00002402606,0.00006163001],"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":[0.00003739106,0.00002432651,0.01701891,0.00007593352,0.00002185545,0.00001565709,0.0002990895,0.8246236,0.01065763,0.007183914,0.008983932,0.1310577],"study_design_scores_gemma":[0.0001643812,0.00002416848,0.0007622427,0.0000212927,0.000004699245,0.00001256847,0.0000142396,0.9729474,0.003324658,0.001083592,0.0214151,0.0002257138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674837,0.000046713,0.003543342,0.00004803628,0.004720025,0.0001421029,0.000005827738,0.000311906,0.02369835],"genre_scores_gemma":[0.9889262,0.000001183553,0.008924099,0.00005504428,0.001860361,0.00003063668,0.00001400654,0.00003627157,0.0001521709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1483237,"threshold_uncertainty_score":0.5204571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355588124205069,"score_gpt":0.2181455192291404,"score_spread":0.1945896379870897,"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."}}