Electricity demand uncertainty modeling with Temporal Convolution Neural Network models

The required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and U...

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Detalles Bibliográficos
Autores: Ghimire, Sujan, Deo, Ravinesh C., Casillas-Pérez, David, Salcedo-Sanz, Sancho, Acharya, Rajendra, Dinh, Toan
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/42179
Acceso en línea:https://hdl.handle.net/10115/42179
Access Level:acceso abierto
Palabra clave:Deep learning
Temporal Convolution Network
Uncertainty analysis
Hybrid models
Long Short-term Memory network
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spelling Electricity demand uncertainty modeling with Temporal Convolution Neural Network modelsGhimire, SujanDeo, Ravinesh C.Casillas-Pérez, DavidSalcedo-Sanz, SanchoAcharya, RajendraDinh, ToanDeep learningTemporal Convolution NetworkUncertainty analysisHybrid modelsLong Short-term Memory networkThe required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and Universities (MICINNU) .This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand ( ) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.Elsevier202420242025info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/42179reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/421792026-06-24T12:48:17Z
dc.title.none.fl_str_mv Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
title Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
spellingShingle Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
Ghimire, Sujan
Deep learning
Temporal Convolution Network
Uncertainty analysis
Hybrid models
Long Short-term Memory network
title_short Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
title_full Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
title_fullStr Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
title_full_unstemmed Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
title_sort Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
dc.creator.none.fl_str_mv Ghimire, Sujan
Deo, Ravinesh C.
Casillas-Pérez, David
Salcedo-Sanz, Sancho
Acharya, Rajendra
Dinh, Toan
author Ghimire, Sujan
author_facet Ghimire, Sujan
Deo, Ravinesh C.
Casillas-Pérez, David
Salcedo-Sanz, Sancho
Acharya, Rajendra
Dinh, Toan
author_role author
author2 Deo, Ravinesh C.
Casillas-Pérez, David
Salcedo-Sanz, Sancho
Acharya, Rajendra
Dinh, Toan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Deep learning
Temporal Convolution Network
Uncertainty analysis
Hybrid models
Long Short-term Memory network
topic Deep learning
Temporal Convolution Network
Uncertainty analysis
Hybrid models
Long Short-term Memory network
description The required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and Universities (MICINNU) .
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/42179
url https://hdl.handle.net/10115/42179
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419109296373761
score 15,811543

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