{"id":"W2897479513","doi":"10.1109/innovate-data.2018.00014","title":"Towards Hybrid Energy Consumption Prediction in Smart Grids with Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Energy consumption; Smart grid; Thunder; Implementation; Machine learning; Artificial intelligence; Data modeling; Multivariate statistics; Extreme learning machine; Consumption (sociology); Data mining; Real-time computing; Artificial neural network; 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":[0.001389864,0.0007046668,0.001040477,0.0006380164,0.0003324753,0.001138012,0.0008113346,0.0007144167,0.0006039652],"category_scores_gemma":[0.00333658,0.0004077045,0.000402146,0.0009661986,0.0003789924,0.001937723,0.0009659795,0.001036865,0.000331698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004818265,"about_ca_system_score_gemma":0.0005118391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005024464,"about_ca_topic_score_gemma":0.005336612,"domain_scores_codex":[0.999523,0.0001540999,0.00003169969,0.0001210376,0.0001292927,0.00004090533],"domain_scores_gemma":[0.9987481,0.0006912927,0.0001366507,0.0002032268,0.0001862612,0.00003446738],"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":[0.0001947744,0.0001459928,0.004648113,0.00005519786,0.0001159098,0.00007274287,0.00007933883,0.8093646,0.004007218,0.003692681,0.001039601,0.1765838],"study_design_scores_gemma":[0.000001611842,0.000007442113,0.0001324193,0.000001504437,0.000002566714,0.00000413336,0.000002604572,0.9983208,0.0003602602,0.001061689,0.0001029482,0.000002061111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06295502,0.0004594575,0.932752,0.0002993657,0.00004976878,0.00002861979,0.00008417176,0.002050485,0.001321067],"genre_scores_gemma":[0.8715876,0.0001890944,0.1265198,0.0001126462,0.00008971542,0.00004895251,0.0001719376,0.00007264268,0.001207612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005024464,"threshold_uncertainty_score":0.009990454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009188912074757472,"score_gpt":0.1902586320650487,"score_spread":0.1810697199902912,"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."}}