Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
Article
Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David, Salcedo-Sanz, Sancho, Acharya, Rajendra and Dinh, Toan. 2025. "Electricity demand uncertainty modeling with Temporal Convolution Neural Network models." Renewable and Sustainable Energy Reviews. 209. https://doi.org/10.1016/j.rser.2024.115097
| Article Title | Electricity demand uncertainty modeling with Temporal Convolution Neural Network models |
|---|---|
| ERA Journal ID | 4066 |
| Article Category | Article |
| Authors | Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David, Salcedo-Sanz, Sancho, Acharya, Rajendra and Dinh, Toan |
| Journal Title | Renewable and Sustainable Energy Reviews |
| Journal Citation | 209 |
| Article Number | 115097 |
| Number of Pages | 28 |
| Year | 2025 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 1364-0321 |
| 1879-0690 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.rser.2024.115097 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1364032124008232 |
| Abstract | This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand (G) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of G 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. |
| Keywords | Deep learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461104. Neural networks |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| Rey Juan Carlos University, Spain | |
| University of Alcala, Spain | |
| Centre for Future Materials |
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https://research.usq.edu.au/item/zx13z/electricity-demand-uncertainty-modeling-with-temporal-convolution-neural-network-models
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