DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.039
Publication date: 9th January 2023
Precise and reliable measurement of wind power uncertainty is crucial for the efficient and effective operation of the smart grid. This is because it allows for better economic planning and real-time control of the grid, ensuring a stable and reliable supply of energy. In this paper, a novel spiking neural network (SNN) architecture is proposed as a solution to this problem. SNNs are an alternative to artificial neural networks (ANNs) that encode interneuron communication into temporally-distributed spikes. This allows for a reduction in memory access frequency and data communication, leading to lower computational power requirements for deep learning workloads.
The proposed SNN architecture uses a multi-objective gradient descent (MOGD) algorithm to generate high-quality wind power prediction intervals (PIs). In this algorithm, the SNN is trained to optimize multiple, potentially conflicting objectives simultaneously. The use of MOGD in the proposed SNN architecture makes it well-suited for tasks with multiple, potentially conflicting objectives, such as multimodal data analysis or optimization of multiple objectives.
The use of SNNs is especially important in remote locations where many wind power farms are located. These locations often lack access to reliable cloud services and power supplies, making the reduced computational power requirements of SNNs particularly advantageous. Because SNNs encode interneuron communication into temporally-distributed spikes, they require less memory access and data communication, leading to lower computational power requirements for deep learning workloads. This makes them well-suited for use in remote locations where access to computational resources may be limited.
The proposed SNN architecture is able to achieve comparable performance to its ANN counterpart on complex regression tasks, such as wind power interval prediction. In these tasks, the resulting multi-objective SNN demonstrates superior performance compared to state-of-the-art ANNs. This makes it a promising solution for improving the precision and reliability of wind power uncertainty measurement in the smart grid.
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