{"id":"W4401671684","doi":"10.1016/j.apenergy.2024.124141","title":"Efficient demand response location targeting for price spike mitigation by exploiting price-demand relationship","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Demand response; Spike (software development); On demand; Economics; Industrial organization; Microeconomics; Econometrics; Business; Natural resource economics; Commerce; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007845453,0.0002246087,0.000151412,0.0002047373,0.0001770914,0.0001221377,0.0001250347,0.0001128939,0.00001438996],"category_scores_gemma":[0.0001115528,0.0002497603,0.00005253426,0.000553327,0.00002297723,0.00008708041,0.00003955158,0.0001127586,0.00003101759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668082,"about_ca_system_score_gemma":0.00002363287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006215738,"about_ca_topic_score_gemma":0.00000188611,"domain_scores_codex":[0.9985914,0.00003990455,0.0003891799,0.000376594,0.0002323778,0.0003705875],"domain_scores_gemma":[0.9990044,0.0005986649,0.00004943635,0.0002208635,0.00004418628,0.00008248906],"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":[0.0000382971,0.00001528619,0.00001220728,0.0001903224,0.00004378127,0.000001438715,0.0002453674,0.9171054,0.02663401,0.04009132,0.01487386,0.0007487114],"study_design_scores_gemma":[0.0003305966,0.00002348195,0.0003135707,0.00008826937,0.00004272435,0.00000144878,0.0001821876,0.8522969,0.03012276,0.0009823947,0.1152607,0.0003548675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1416088,0.001256041,0.8505757,0.0001892291,0.0005607859,0.0002609338,0.00000575866,0.0009261216,0.004616633],"genre_scores_gemma":[0.9941874,0.00002735747,0.004032795,0.00008227537,0.0003357172,0.0006458727,0.0001645609,0.0001011373,0.0004228861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8525786,"threshold_uncertainty_score":0.9999955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00787546134417623,"score_gpt":0.204706744487752,"score_spread":0.1968312831435758,"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."}}