{"id":"W4401069949","doi":"10.1016/j.apenergy.2024.123988","title":"Residential consumer enrollment in demand response: An agent based approach","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Strategic Research Council","keywords":"Flexibility (engineering); Quarter (Canadian coin); Demand response; Environmental economics; Business; Energy (signal processing); Consumer demand; Economics; Marketing; Microeconomics; Engineering; Electricity; Geography; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004126875,0.000243738,0.000197471,0.0003820858,0.00003671457,0.0001065095,0.0002108775,0.0001040596,0.0001152957],"category_scores_gemma":[0.000004138421,0.0002551056,0.00005208747,0.000397078,0.00003427839,0.00008641853,0.00005737399,0.0001356301,0.00005128114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000216396,"about_ca_system_score_gemma":0.00003437069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009874049,"about_ca_topic_score_gemma":0.00009613381,"domain_scores_codex":[0.9985596,0.00006725196,0.0002963608,0.000414534,0.0002785866,0.0003836892],"domain_scores_gemma":[0.9993669,0.00007375032,0.00001242299,0.0004293277,0.000007126577,0.0001104756],"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":[0.0001293458,0.00005994586,0.00001893973,0.00006874915,0.0000703429,0.00007189182,0.0001056277,0.9665083,0.006508293,0.01706759,0.006849054,0.002541898],"study_design_scores_gemma":[0.0008756335,0.0000411014,0.001475213,0.0000353411,0.00004126814,0.000003654003,0.00008325352,0.7067627,0.01625389,0.0004129877,0.2734967,0.0005182077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3729027,0.002809642,0.5302852,0.000198549,0.00253707,0.0004892288,0.00001443821,0.002653536,0.08810963],"genre_scores_gemma":[0.9969864,0.00005934688,0.001722173,0.0001507246,0.00017375,0.0003498606,0.00005958385,0.00008548457,0.0004127001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6240836,"threshold_uncertainty_score":0.9999901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00970110540018108,"score_gpt":0.2078052116385689,"score_spread":0.1981041062383878,"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."}}