Neuromorphic computing for short-term wind power forecasting

There is an upwards trend of applying deep learning to model wind power forecasts. The modelling and training of these architectures may take many computational resources, hindering the possibility of implementing such algorithms for shorter term prediction horizons. Emerging computational architectures such as neuromorphic computing have the potential of real-time learning using brain-inspired algorithms characterized by low latency and low energy consumption. In particular, we introduce spiking neural networks for short-term wind power forecasting, taking into consideration the current development and features of neuromorphic devices.