{"id":"W2779684463","doi":"","title":"Load Scheduling for Residential Demand Response on Smart Grids","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Demand response; Computer science; Time horizon; Scheduling (production processes); Mathematical optimization; Schedule; Smart grid; Load management; Electric power system; Linear programming; Grid; Integer programming; Photovoltaic system; Electricity; Energy consumption; Power (physics); Engineering; 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":[0.008487809,0.0004570535,0.0004362857,0.0002696557,0.000525872,0.0007219783,0.001635876,0.000360943,0.00005648318],"category_scores_gemma":[0.003120147,0.0005362532,0.0002928737,0.0001186208,0.0001449541,0.000141321,0.001108667,0.0006331556,0.00007157772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000407745,"about_ca_system_score_gemma":0.0002228981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003506055,"about_ca_topic_score_gemma":0.0009712944,"domain_scores_codex":[0.9958224,0.001834565,0.0005347231,0.0007765205,0.0005388847,0.0004929584],"domain_scores_gemma":[0.9935827,0.001517404,0.0002934009,0.003352229,0.001072155,0.0001820903],"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":[0.001160666,0.0009272536,0.001502197,0.002527927,0.001559214,0.00006615613,0.01081428,0.7977555,0.01356335,0.07641371,0.05494402,0.03876574],"study_design_scores_gemma":[0.002821607,0.000002726975,0.01299595,0.006702394,0.0002840187,0.000008538049,0.00009550177,0.5919632,0.1524809,0.007397863,0.2232067,0.002040671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3278966,0.001504351,0.5996333,0.007779387,0.003300858,0.001299247,0.0001283082,0.001254029,0.05720391],"genre_scores_gemma":[0.9440466,0.0005958854,0.04223713,0.00006935655,0.000189105,0.0003883971,0.0003211412,0.0001715987,0.01198085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6161499,"threshold_uncertainty_score":0.9997089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644792041162674,"score_gpt":0.2355714024890322,"score_spread":0.2191234820774055,"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."}}