{"id":"W2952133925","doi":"10.1016/j.petrol.2019.106187","title":"Conventional models and artificial intelligence-based models for energy consumption forecasting: A review","year":2019,"lang":"en","type":"review","venue":"Journal of Petroleum Science and Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":255,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"China Scholarship Council","keywords":"Artificial neural network; Predictive modelling; Mean absolute percentage error; Computer science; Energy consumption; Benchmark (surveying); Model selection; Machine learning; Artificial intelligence; Consumption (sociology); 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":[0.001371988,0.00133888,0.001705878,0.001835264,0.0002141236,0.001493847,0.001951459,0.001444823,0.001930633],"category_scores_gemma":[0.002733123,0.0003975122,0.0009529109,0.003851386,0.0004662715,0.002116082,0.0006202147,0.001380259,0.001019456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006585139,"about_ca_system_score_gemma":0.001539323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004339868,"about_ca_topic_score_gemma":0.003294753,"domain_scores_codex":[0.9996946,0.00005047849,0.00004553885,0.0000766297,0.0001155324,0.00001738067],"domain_scores_gemma":[0.9983365,0.001118634,0.0001531679,0.00005094376,0.0003035116,0.00003732147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000790363,0.000135858,0.0009572614,0.0153931,0.0002459456,0.00009043461,0.00004628618,0.008607415,0.0006598225,0.01084092,0.01943916,0.9435046],"study_design_scores_gemma":[0.00006406329,0.000431037,0.00412201,0.01218327,0.001547447,0.0008524285,0.0002463872,0.02904891,0.002185929,0.03187035,0.9172481,0.0002001752],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005981905,0.992448,0.004319889,0.0007081529,0.0003776996,0.00001335162,0.0001273548,0.00003706459,0.001370302],"genre_scores_gemma":[0.00429517,0.992189,0.002404497,0.0001961557,0.0003869247,0.00001402642,0.0001434315,0.000005695295,0.0003649499],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004339868,"threshold_uncertainty_score":0.008629203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1341996585916335,"score_gpt":0.2977740448513552,"score_spread":0.1635743862597217,"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."}}