{"id":"W2954802233","doi":"10.1109/isgt.2019.8791595","title":"Smart Households Demand Response Management with Micro Grid","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Demand response; Smart grid; Schedule; Photovoltaic system; Incentive; Computer science; Load management; Peak demand; Grid; Electricity; Energy management; Environmental economics; Energy management system; Operations research; Energy (signal processing); Engineering; Economics; Microeconomics; Electrical 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":[0.0004812646,0.0006102223,0.0004653622,0.0003007033,0.00004246757,0.0001573154,0.0006280178,0.0002453086,0.0001933559],"category_scores_gemma":[0.000003741676,0.00054406,0.000128588,0.0001754111,0.0000377656,0.00007557323,0.001041636,0.0004727946,0.0004708414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003094875,"about_ca_system_score_gemma":0.00002381825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003496813,"about_ca_topic_score_gemma":0.00003536461,"domain_scores_codex":[0.997929,0.00007167825,0.0003719649,0.0006774319,0.0004103605,0.0005396013],"domain_scores_gemma":[0.9981154,0.00005714444,0.00006019482,0.001616795,0.00003206571,0.0001184322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000209377,0.00002789072,0.0009506778,0.0007340045,0.0007680759,0.0001285052,0.00005340638,0.8993216,0.0000940862,0.0002077999,0.09727123,0.0002333783],"study_design_scores_gemma":[0.002332867,0.0001773005,0.0752531,0.0008829329,0.0005996426,0.0000290446,0.000176076,0.04597636,0.005441926,0.0001619859,0.8661798,0.002788995],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5608381,0.0009248193,0.1680127,0.0005396737,0.009946251,0.002495101,0.00006601821,0.004553253,0.2526241],"genre_scores_gemma":[0.9103161,0.001040861,0.03673267,0.0006042391,0.0007091357,0.0006304054,0.0001932954,0.0005988637,0.04917441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8533452,"threshold_uncertainty_score":0.9997011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008703546745098647,"score_gpt":0.1875338546052487,"score_spread":0.17883030786015,"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."}}