Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model
Article
Ghimire, Sujan, Deo, Ravinesh C., Pourmousavi, S. Ali, Casillas-Perez, David and Salcedo-sanz, Sancho. 2024. "Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model." Engineering Applications of Artificial Intelligence. 135. https://doi.org/10.1016/j.engappai.2024.108702
Article Title | Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model |
---|---|
ERA Journal ID | 32032 |
Article Category | Article |
Authors | Ghimire, Sujan, Deo, Ravinesh C., Pourmousavi, S. Ali, Casillas-Perez, David and Salcedo-sanz, Sancho |
Journal Title | Engineering Applications of Artificial Intelligence |
Journal Citation | 135 |
Article Number | 108702 |
Number of Pages | 28 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0952-1976 |
1873-6769 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2024.108702 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0952197624008601 |
Abstract | Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilizing the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models. |
Keywords | Convolutional Neural Networks; Time-series prediction; Deep Learning; Kernel Density Estimation; Gated Recurrent Unit; Neural Facebook Prophet |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Byline Affiliations | School of Mathematics, Physics and Computing |
University of Adelaide | |
Rey Juan Carlos University, Spain | |
University of Alcala, Spain |
Permalink -
https://research.usq.edu.au/item/z84wq/point-based-and-probabilistic-electricity-demand-prediction-with-a-neural-facebook-prophet-and-kernel-density-estimation-model
Download files
11
total views5
total downloads4
views this month1
downloads this month