A multi-scale spatiotemporal spiking neural model for power load forecasting considering extreme weather impact
The increasing frequency of extreme weather events has brought about significant mutation in the distribution characteristics of power load, while traditional models are unable to handle such sudden changes in load and adequately characterize the coupling effects across various scales. To address th...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:dnet:idus________::bda41e64fbea5a81db6b7fced4d4d1db |
| Acceso en línea: | https://hdl.handle.net/11441/184545 https://doi.org/10.1016/j.ijepes.2026.111604 |
| Access Level: | acceso abierto |
| Palabra clave: | Load forecasting Extreme weather Nonlinear spiking neural P systems Bidirectional spiking neural P systems |
| Sumario: | The increasing frequency of extreme weather events has brought about significant mutation in the distribution characteristics of power load, while traditional models are unable to handle such sudden changes in load and adequately characterize the coupling effects across various scales. To address this problem, this study proposes a bidirectional nonlinear spiking neural P (NSNP) model with weather-aware multi-scale fusion, which represents an enhanced NSNP framework that integrates multi-scale adaptive feature extraction network (MAFEN) and multiple encoders based on bidirectional NSNP (BiNSNP) variants, termed multi-scale spatiotemporal BiNSNP attention fusion network (MSBAF-Net). Inspired by nonlinear spiking mechanisms, this architecture captures complex nonlinear load dynamics. Moreover, this multi-source data parallel fusion network effectively achieves dynamic weighting of features across both spatial and temporal dimensions, thereby capturing local patterns at critical time steps in load sequences and cross-channel feature correlations under extreme weather. Specifically, MSBAF-Net performs channel separation, isolating the abrupt components of the load into the residual channel. Based on the characteristics of different channels, MSBAF-Net incorporates a targeted bidirectional modeling strategy alongside differentiated feature extraction pathways, implemented through two lightweight NSNP-like convolutional models. Additionally, feature fusion network (FFN) maintains the interaction of multi-scale load features in time and space. Finally, comparison study using three real-world datasets and 25 baseline prediction models is performed. Experimental results demonstrate that MSBAF-Net achieves the best comprehensive performance across all extreme weather scenarios. Notably, under the low-temperature cold wave scenario, MSBAF-Net achieves average forecasting accuracies of 97.51% and 97.38% for Lines 1–10 at the power station A and Lines 1–7 at the power station B, respectively. Our codes and datasets have been released at https://github.com/hssinne/MSBAF-Net. |
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