{"id":"W4317726571","doi":"10.3390/en16031182","title":"Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Prosumer; Demand response; Dynamic pricing; Heuristic; Computer science; Smart grid; Distributed generation; Key (lock); Business; Electricity; Marketing; Renewable energy; Computer security; Artificial intelligence","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":[0.00137627,0.0008658771,0.001904234,0.0005675134,0.0006465791,0.002077239,0.001565839,0.001947259,0.004172744],"category_scores_gemma":[0.002376022,0.0006449048,0.0007657803,0.0005282389,0.001167018,0.001225821,0.001098757,0.001550857,0.0002744701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556823,"about_ca_system_score_gemma":0.001865746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199089,"about_ca_topic_score_gemma":0.0103765,"domain_scores_codex":[0.9995275,0.0001648596,0.0000191073,0.000100704,0.00008411599,0.000103805],"domain_scores_gemma":[0.9986107,0.0008763715,0.0001321423,0.00005963135,0.0002135974,0.0001075671],"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":[0.00002359616,0.00002183331,0.0001645652,0.0000102294,0.00000842541,0.00003680651,0.00001228997,0.9966156,0.00008640045,0.001671582,0.0001525343,0.001196234],"study_design_scores_gemma":[0.000003741341,0.000005800923,0.00002167933,9.823781e-7,0.000002231412,0.000002329612,0.000003421507,0.9994916,0.0000205597,0.000416832,0.00002929392,0.000001559224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1968512,0.0006273087,0.7758158,0.00124932,0.0001354581,0.0002671343,0.0002761887,0.0003922854,0.02438543],"genre_scores_gemma":[0.9831789,0.0001050014,0.01335927,0.00007644803,0.00001701499,0.00009171664,0.00005412867,0.00001798428,0.003099559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01199089,"threshold_uncertainty_score":0.02384216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338292522863075,"score_gpt":0.2163192836655005,"score_spread":0.2029363584368697,"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."}}