Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks

Wei, Danxiang; Wang, Jianzhou; Niu, Xinsong; Li, Zhiwu

doi:10.1016/j.apenergy.2021.116842

Cited by 68 publications

(8 citation statements)

References 34 publications

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“…The training process can be implemented using frameworks like Nengo [ 39 ], CARLsim [ 40 ], and Brain2 [ 41 ]. For clarity, we use the commonly adopted convolutional SNNs for recognition applications in Figure 3 , primarily used for classification tasks [ 42 ].…”

Section: Framework and Design Methodsmentioning

confidence: 99%

EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing

Xue

Li-sheng

Chen

et al. 2023

Sensors

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Spiking neural networks (SNNs) have attracted considerable attention as third-generation artificial neural networks, known for their powerful, intelligent features and energy-efficiency advantages. These characteristics render them ideally suited for edge computing scenarios. Nevertheless, the current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times, low throughput, and insufficient consideration of energy consumption and connectivity, which undermine their suitability for edge computing applications. To address these challenges, we introduce EdgeMap, an optimized mapping toolchain specifically designed for deploying SNNs onto edge devices without compromising performance. EdgeMap consists of two main stages. The first stage involves partitioning the SNN graph into small neuron clusters based on the streaming graph partition algorithm, with the sizes of neuron clusters limited by the physical neuron cores. In the subsequent mapping stage, we adopt a multi-objective optimization algorithm specifically geared towards mitigating energy costs and communication costs for efficient deployment. EdgeMap—evaluated across four typical SNN applications—substantially outperforms other state-of-the-art mapping schemes. The performance improvements include a reduction in average latency by up to 19.8%, energy consumption by 57%, and communication cost by 58%. Moreover, EdgeMap exhibits an impressive enhancement in execution time by a factor of 1225.44×, alongside a throughput increase of up to 4.02×. These results highlight EdgeMap’s efficiency and effectiveness, emphasizing its utility for deploying SNN applications in edge computing scenarios.

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Section: Framework and Design Methodsmentioning

confidence: 99%

EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing

Xue

Li-sheng

Chen

et al. 2023

Sensors

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“…A plethora of efficient computer vision applications using SNNs are reviewed in [49]. SNNs are equally suitable to track objects such as satellites in the sky for space situational awareness [50], [51] and have been researched to promote sustainable uses of artificial intelligence, such as in monitoring material strain in smart buildings [52] and wind power forecasting in remote areas that face power delivery challenges [53]. At the 2018-19 Telluride Neuromorphic and Cognition Workshops, a neuromorphic robot was even built to play foosball!…”

Section: B Neuromorphic Systems In the Wildmentioning

confidence: 99%

Training Spiking Neural Networks Using Lessons From Deep Learning

Eshraghian,

Ward,

Neftci

et al. 2023

Proc. IEEE

224

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“…AI models have been widely used in time series forecasting ( Nie et al, 2020 ; Wang, Wang, Lu et al 2021 ; Jiang et al, 2021 ; Niu & Wang, 2019 ; Wang et al, 2021 ). AI models, including artificial neural networks (ANNs; Zhang Wang & Niu 2021 ) and other technologies ( Wei et al, 2021 ), possess excellent nonlinear fitting ability to better capture the nonlinearity and randomness of the tourism demand time series. The echo state network (ESN) is a traditional ANN that is widely used in forecasting fields ( Qin et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

Wang

Zhang

Liu

et al. 2023

Journal of Hospitality & Tourism Research

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A hybrid tourism demand interval forecasting system is proposed consisting of two parts: the construction of forecasting interval based on lower and upper bound estimates, and the forecasting interval adjustment based on an optimized reduction coefficient. Coronavirus factors are added as input variables to improve forecasting performance. A new multi-objective optimization algorithm is proposed to construct a feature selection method, optimize the forecasting model, and estimate the optimal reduction coefficient. The results of the experiments show that the proposed system has a powerful interval forecasting ability, which provides crucial guidance for balancing the recovery of the tourism industry and the control of the epidemic spread during the COVID-19 pandemic, and contributes to contingency planning for tourism practitioners and managers.

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Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks

Cited by 68 publications

References 34 publications

EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing

EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing

Training Spiking Neural Networks Using Lessons From Deep Learning

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

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