{"id":"W4385477867","doi":"10.1109/cai54212.2023.00046","title":"Home Energy Management with V2X Capability using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Electricity; Computer science; Energy management; Control (management); Electricity generation; Home automation; Energy management system; Load management; Electric power system; Energy (signal processing); Power (physics); Engineering; Artificial intelligence; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004435289,0.0003997331,0.0003842481,0.0001739305,0.0002447015,0.0004932624,0.0005677403,0.0003126158,0.001893799],"category_scores_gemma":[0.0007904393,0.0001488391,0.0001879499,0.0001342105,0.0003028622,0.0004130355,0.0006356457,0.0005087471,0.0002957997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003187955,"about_ca_system_score_gemma":0.0003940031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004923515,"about_ca_topic_score_gemma":0.003890348,"domain_scores_codex":[0.9998538,0.00004313214,0.00000846098,0.00003226738,0.00003937569,0.00002289217],"domain_scores_gemma":[0.9997314,0.000121861,0.0000400341,0.00002944835,0.00005621917,0.0000210496],"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":[0.000193456,0.0001641364,0.0009777882,0.0000622948,0.00004797056,0.0001188842,0.00006818524,0.894393,0.008457122,0.004318137,0.001433216,0.08976586],"study_design_scores_gemma":[0.00001975087,0.00004668843,0.0001100966,0.000003105151,0.000003542834,0.00001326418,0.000003700719,0.9972319,0.0009219953,0.001210827,0.0004319997,0.000003049917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1012208,0.0003342392,0.8810865,0.000324826,0.0000767332,0.000123713,0.00004607192,0.002527303,0.01425983],"genre_scores_gemma":[0.9839897,0.00003931735,0.01457857,0.00003456019,0.000009626609,0.00003608053,0.00001667038,0.00001928461,0.001276117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004923515,"threshold_uncertainty_score":0.009789705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027508173219408,"score_gpt":0.190971900949268,"score_spread":0.1806968192170739,"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."}}