{"id":"W4362653285","doi":"10.1109/ojia.2023.3264651","title":"A Stochastic Approach to Integrating Electrical Thermal Storage in Distributed Demand Response for Nordic Communities With Wind Power Generation","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Industry Applications","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demand response; Flexibility (engineering); Distributed generation; Computer science; Thermal energy storage; Wind power; Peak demand; Grid; Energy storage; Renewable energy; Distributed data store; Smart grid; Distributed computing; Power (physics); Engineering; Electrical engineering; Electricity; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008772404,0.0001367417,0.0002082369,0.0002959845,0.0001386829,0.0001381204,0.0005001715,0.0001122391,0.000006897691],"category_scores_gemma":[0.0000417188,0.0001201475,0.00003135286,0.0009588688,0.00002427152,0.0002132644,0.00004895755,0.0005540539,0.000003545816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969342,"about_ca_system_score_gemma":0.0000817965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670453,"about_ca_topic_score_gemma":0.00002438993,"domain_scores_codex":[0.999048,0.00009320596,0.0003721042,0.0001012762,0.0001622684,0.0002231776],"domain_scores_gemma":[0.9992508,0.0001986795,0.00009699501,0.0002382634,0.0001166174,0.00009868367],"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":[0.0001515879,0.00005986486,0.0001012647,0.000005367571,0.00005490929,0.000002622497,0.0006561839,0.9909613,0.004744532,0.0002154057,0.00279742,0.0002495149],"study_design_scores_gemma":[0.002856702,0.0006589388,0.03038795,0.000218298,0.0001080937,0.000102215,0.006055079,0.9508017,0.001764003,0.000080566,0.006308136,0.0006583476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5270333,0.000009426617,0.4719823,0.0001675428,0.00004402285,0.0005668651,0.00001983802,0.00002377787,0.0001529771],"genre_scores_gemma":[0.9957849,0.00000121761,0.003383975,0.00004254754,0.0001439533,0.0005103557,0.00004094045,0.00003257192,0.00005950344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4687516,"threshold_uncertainty_score":0.4899473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675820603051019,"score_gpt":0.2752949790305711,"score_spread":0.2385367730000609,"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."}}