Rehman, Hafiz Muhammad Raza Ur and Younas, Rabbiya and Changhyun, Park and Gul, Urfa and Álvarez, Roberto Marcelo and Miró Vera, Yini Airet and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, roberto.alvarez@uneatlantico.es, yini.miro@uneatlantico.es, UNSPECIFIED (2026) A Hybrid Temporal-spectral Load Forecasting Model with Static Context Fusion for Smart Cities. International Journal of Computational Intelligence Systems. ISSN 1875-6883
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Abstract
Accurate short-term electricity load forecasting is essential for reliable and efficient smart city energy management, particularly in environments characterized by high-dimensional, heterogeneous, and noisy multivariate signals. However, existing forecasting models often struggle to simultaneously capture nonlinear temporal dependencies, multi-scale periodicity, and static contextual influences within a unified framework. To address this challenge, this study proposes a hybrid deep learning architecture that integrates Bidirectional Long Short-Term Memory (BiLSTM) for temporal modeling, an additive attention mechanism for adaptive time-step weighting, Fast Fourier Transform (FFT)-based frequency residual learning for periodicity extraction, and embedding-based static feature fusion for contextual representation. The model is evaluated on the ISO-NE Smart City Energy Dataset for next-hour electricity load forecasting using a two-week input window (336 hours). Experimental results demonstrate that the proposed hybrid framework significantly improves predictive accuracy, achieving an RMSE of 25.51 kW and an R of 0.9905, outperforming recurrent, convolutional, and transformer-based baselines under identical evaluation settings. Ablation analysis confirms that temporal attention and frequency-domain residual modeling contribute substantially to performance gains. These findings indicate that joint temporal–spectral modeling combined with static contextual fusion provides a robust and effective solution for complex smart-city electricity forecasting tasks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Smart city energy forecasting Electricity load prediction Temporal attention Fourier transform Hybrid deep learning Frequency-domain learning Static feature fusion Renewable-aware forecasting Urban energy analytics |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Articles and books Ibero-american International University > Research > Articles and books Ibero-american International University > Research > Articles and Books Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 17 Jul 2026 08:25 |
| Last Modified: | 17 Jul 2026 08:25 |
| URI: | https://repositorio.funiber.org/id/eprint/28808 |
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