{"id":"W1968785194","doi":"10.1109/vetecf.2010.5594388","title":"TOU-Aware Energy Management and Wireless Sensor Networks for Reducing Peak Load in Smart Grids","year":2010,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Smart grid; Environmental economics; Energy management; Computer science; Peak demand; Electricity; Energy consumption; Greenhouse gas; Demand response; Base station; Distributed generation; Load management; Mains electricity; Wireless sensor network; Telecommunications; Renewable energy; Computer network; Energy (signal processing); Electrical engineering; Engineering; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001967178,0.0002173922,0.000196134,0.0001390587,0.00005139327,0.00005890496,0.0001465781,0.0001000617,0.0000513416],"category_scores_gemma":[0.000004541964,0.0002228712,0.00004386576,0.0001741988,0.00002482663,0.0001047754,0.0001163782,0.0001497114,0.00000340838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008248954,"about_ca_system_score_gemma":0.000004955489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002368924,"about_ca_topic_score_gemma":0.001756405,"domain_scores_codex":[0.9988587,0.00001015749,0.000252351,0.0003138428,0.0001403161,0.0004245774],"domain_scores_gemma":[0.9994786,0.00004697811,0.00001949799,0.0003407944,0.00002546142,0.00008868931],"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":[0.00002865705,0.00004644904,0.001197224,0.0002536691,0.0001552861,0.00004152305,0.0001110963,0.8971793,0.001011061,0.01689677,0.0222293,0.06084968],"study_design_scores_gemma":[0.000905049,0.00002321377,0.00743283,0.00004690484,0.00002730299,0.000005441711,0.0001364525,0.8914481,0.0008896146,0.00007626103,0.09861665,0.0003921727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6374449,0.000233966,0.2919178,0.0002941863,0.005094593,0.0007001681,0.000005885652,0.001010826,0.06329773],"genre_scores_gemma":[0.9910449,0.00020156,0.004258877,0.000125458,0.000379545,0.0001766821,0.00001672561,0.00006578294,0.003730485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3536001,"threshold_uncertainty_score":0.9088421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00492580261137507,"score_gpt":0.1839821976706741,"score_spread":0.179056395059299,"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."}}