{"id":"W4206910610","doi":"10.3390/en15030810","title":"CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Overfitting; Computer science; Autoencoder; Robustness (evolution); Artificial intelligence; Deep learning; Mean squared error; Convolutional neural network; Machine learning; Energy consumption; Artificial neural network; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005223124,0.001337884,0.0004980391,0.0007137906,0.000206879,0.0005993873,0.00157822,0.0008144245,0.001789362],"category_scores_gemma":[0.00113518,0.0003464116,0.0006223103,0.0009360443,0.0002521733,0.001305137,0.0007611053,0.001442622,0.0007448458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008303192,"about_ca_system_score_gemma":0.0009574084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02668621,"about_ca_topic_score_gemma":0.04669468,"domain_scores_codex":[0.9998013,0.00003100526,0.00001053947,0.00007174365,0.00005632367,0.00002906298],"domain_scores_gemma":[0.9998028,0.00005357808,0.00001925677,0.00002563326,0.00008764103,0.0000111696],"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":[0.0001862965,0.0002191725,0.00395433,0.0001658103,0.0002042334,0.0001257547,0.00006459983,0.7034028,0.008121825,0.005375489,0.00983227,0.2683475],"study_design_scores_gemma":[0.000003289205,0.00001257603,0.0003863875,0.000007918014,0.000007093154,0.00001047461,0.00000502743,0.9958659,0.001290733,0.001619155,0.0007852545,0.000006210772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04879066,0.001139572,0.9370552,0.0004045571,0.0001671052,0.00006818515,0.002946823,0.005995133,0.00343276],"genre_scores_gemma":[0.6871469,0.0009804049,0.2942049,0.0004729138,0.0001083449,0.0002169549,0.007843247,0.0003060982,0.00872023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02668621,"threshold_uncertainty_score":0.05306172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105521969016419,"score_gpt":0.2148248886772208,"score_spread":0.2037696689870566,"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."}}