{"id":"W3120800000","doi":"10.15173/esr.v24i1.4135","title":"A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity","year":2020,"lang":"en","type":"article","venue":"Energy Studies Review","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Electricity; Dual (grammatical number); Wavelet; Process (computing); Autoregressive conditional heteroskedasticity; Empirical research; Electricity market; Electricity price forecasting; Wavelet transform; Econometrics; Artificial intelligence; Machine learning; Economics; Mathematics; Volatility (finance); Statistics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000671313,0.0005535838,0.0004883168,0.0006718682,0.0001682913,0.0005892804,0.0007689855,0.0005915217,0.0008318548],"category_scores_gemma":[0.001054939,0.0001900437,0.0004617238,0.001026204,0.0001796783,0.001308706,0.0005290366,0.0006657553,0.000237849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000305762,"about_ca_system_score_gemma":0.0003354879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00194346,"about_ca_topic_score_gemma":0.002019082,"domain_scores_codex":[0.9997831,0.00005733107,0.00001425995,0.00005028059,0.00007571094,0.00001929753],"domain_scores_gemma":[0.9998471,0.00007218039,0.00001722989,0.00001184238,0.00004488186,0.000006718671],"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":[0.00009043478,0.00009823506,0.002613015,0.0002140701,0.000164151,0.0001413826,0.00008097776,0.625965,0.007584841,0.02732706,0.00140323,0.3343177],"study_design_scores_gemma":[0.000002287457,0.00001187982,0.0001697892,0.000003696918,0.000007956487,0.00001409769,0.000003922377,0.9970251,0.0003360379,0.00201227,0.0004102038,0.000002789912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01715839,0.0008019302,0.9799736,0.000123872,0.0000790573,0.00001813159,0.00004801556,0.0001224981,0.001674451],"genre_scores_gemma":[0.6767634,0.002265648,0.3135511,0.0001090625,0.0002098698,0.0001037683,0.0002838626,0.00005514591,0.006658239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00194346,"threshold_uncertainty_score":0.003864288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06644680494165515,"score_gpt":0.2514001057935716,"score_spread":0.1849533008519164,"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."}}