{"id":"W3145208339","doi":"10.3390/designs5020027","title":"Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning","year":2021,"lang":"en","type":"article","venue":"Designs","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Support vector machine; Mean absolute percentage error; Demand response; Random forest; Artificial intelligence; Electricity; Renewable energy; Term (time); Artificial neural network; Electrical load; Machine learning; Environmental economics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002356202,0.0002188353,0.000230483,0.00004860488,0.0002806452,0.00007768787,0.00007994586,0.0001126275,0.0001118512],"category_scores_gemma":[0.0002516027,0.0002331436,0.00006403436,0.0003061339,0.00003328957,0.0001897212,0.00005110764,0.0006180516,0.00001022188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007868285,"about_ca_system_score_gemma":0.00004150919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002686719,"about_ca_topic_score_gemma":0.00008133042,"domain_scores_codex":[0.9987358,0.00008697729,0.0002342085,0.0002721233,0.000233781,0.0004371031],"domain_scores_gemma":[0.9993992,0.0002867411,0.00005165414,0.00009246598,0.00007413699,0.00009579738],"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":[0.00009879517,0.000121793,0.1027835,0.0008272579,0.0006613689,0.001652733,0.01563825,0.3740482,0.2100097,0.00362593,0.000804613,0.2897278],"study_design_scores_gemma":[0.001086631,0.0001719733,0.001999918,0.0002931902,0.00008361026,0.001001512,0.0003099358,0.8903745,0.03689022,0.0006785685,0.06613829,0.0009715892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296675,0.0263146,0.201303,0.0001516725,0.0006436084,0.0001614658,0.000003948542,0.001427625,0.04032657],"genre_scores_gemma":[0.9917388,0.001010413,0.006159946,0.00006214517,0.0002583073,0.000008614308,0.00004473736,0.00006331888,0.0006537444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5163263,"threshold_uncertainty_score":0.9507319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03787378881506236,"score_gpt":0.2355935002248618,"score_spread":0.1977197114097995,"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."}}