{"id":"W2984772927","doi":"10.1007/s00202-019-00866-x","title":"An efficient hybrid structure to solve economic-environmental energy scheduling integrated with demand side management programs","year":2019,"lang":"en","type":"article","venue":"Electrical Engineering","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Environmental economics; Greenhouse gas; Scheduling (production processes); Demand side; Demand response; Electricity; Demand management; Energy management; Electricity generation; Smart grid; Computer science; Business; Operations management; Economics; Engineering; Power (physics); Energy (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006013042,0.000509517,0.0008315207,0.0004650949,0.0004797532,0.0007145634,0.001201359,0.000886545,0.004160195],"category_scores_gemma":[0.001092709,0.0004195007,0.0005566364,0.0005520765,0.0004789714,0.0006782851,0.0009800605,0.0008076759,0.0003178801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006382251,"about_ca_system_score_gemma":0.001677303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004960291,"about_ca_topic_score_gemma":0.007499042,"domain_scores_codex":[0.9997517,0.00007012416,0.00000906868,0.00003947491,0.00006406506,0.0000654674],"domain_scores_gemma":[0.999649,0.0001808557,0.0000295286,0.00003153395,0.00007142717,0.00003758979],"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":[0.00008420351,0.0001203518,0.0002997038,0.00002823678,0.00001879854,0.00003037684,0.00002660339,0.9669748,0.001341208,0.01055267,0.000761618,0.01976143],"study_design_scores_gemma":[0.00001103506,0.00002017879,0.00003287235,0.000001096988,0.000002704809,0.000002260452,0.000003825247,0.9980633,0.0001450729,0.001578475,0.0001378947,0.000001320502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08191525,0.00009759176,0.9085509,0.0002158698,0.00006680629,0.0001273322,0.0001270813,0.0003982171,0.008500857],"genre_scores_gemma":[0.7639009,0.00005831965,0.2308493,0.0000896061,0.00004685146,0.0002571486,0.0002074926,0.00008898573,0.004501358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004960291,"threshold_uncertainty_score":0.01391727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001459042945408396,"score_gpt":0.144252216189021,"score_spread":0.1427931732436127,"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."}}