{"id":"W3013158530","doi":"10.1016/j.energy.2020.118500","title":"Smart Distributed Energy Storage Controller (smartDESC)","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Hydro-Québec; Australian Government","keywords":"Computer science; Controller (irrigation); Renewable energy; Scalability; Distributed computing; Energy storage; Anticipation (artificial intelligence); Scale (ratio); Architecture; Smart grid; Power (physics); Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002964601,0.0004919328,0.0005490208,0.0006420424,0.0006029808,0.0008711436,0.0007985986,0.0004069302,0.02537596],"category_scores_gemma":[0.000501266,0.0001907377,0.000190356,0.0006920429,0.0003119947,0.0007519001,0.0004751916,0.000547472,0.005068075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006511773,"about_ca_system_score_gemma":0.0007650324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762898,"about_ca_topic_score_gemma":0.003453852,"domain_scores_codex":[0.9996889,0.00003296844,0.00002186921,0.00007537113,0.0001539276,0.00002692797],"domain_scores_gemma":[0.9993913,0.00007088819,0.00004326148,0.0001335482,0.0003121693,0.00004889119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001662792,0.0005532311,0.007521765,0.0008379368,0.0001215044,0.000367746,0.0002199076,0.02909393,0.06985254,0.02795286,0.2460677,0.615748],"study_design_scores_gemma":[0.0006603089,0.0008535112,0.009124884,0.0001190254,0.0001669049,0.001262831,0.0001319092,0.3287583,0.1988109,0.01391285,0.4460775,0.0001211094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.2199816,0.003036932,0.290186,0.002054474,0.002252483,0.001232039,0.01143207,0.09101373,0.3788107],"genre_scores_gemma":[0.9204883,0.000326552,0.01806045,0.0006715679,0.000165452,0.0001558994,0.002530178,0.0004281384,0.05717338],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02537596,"threshold_uncertainty_score":0.08489102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008203547721477066,"score_gpt":0.1662015919873614,"score_spread":0.1579980442658843,"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."}}