Neuromorphic Computing-Based Model for Short-Term Forecasting of Global Horizontal Irradiance in Saudi Arabia

To tackle environmental and increasing energy demand issues, different energy transition options have been investigated. Solar power has vast resources and is environment-friendly, making it a possible alternative to fossil fuels. However, its integration into the power system poses many challenges because of its uncertain variability, and different deep-learning techniques have been put forward to address its intermittent nature. However, these techniques pose challenges related to high computational overhead and power requirements. Therefore, we present a deep-learning technique leveraging the Leaky Integrated and Fire (LIF) spiking neurons for short-term forecasting of Global Horizontal Irradiance (GHI). The proposed NeuroSpike network consists of a Recurrent Neural Network (RNN) layer, initialized with LIF spiking neurons and stacked with the conventional Long Short-Term Memory (LSTM) layer. The historical GHI data from three distinct locations in the Kingdom of Saudi Arabia (KSA) is used in this study. In the data preprocessing step, a Recursive Feature Elimination with Categorical Boosting (RFE-CatBoost) algorithm is used to select the appropriate features that inherently describe the dataset patterns. The proposed NeuroSpike network is trained on the selected features, and its forecast performance is compared with different benchmark techniques reported in the literature. The results demonstrate that the NeuroSpike network has lower forecasting errors than the techniques compared. Moreover, with RFE-CatBoost algorithm-based feature selection, an improvement of 30.33%, 43.12%, and 23.4% is recorded in the Mean Absolute Error (MAE) of the NeuroSpike network for the datasets of Al-Jouf, Qassim and K.A.CARE sites, respectively. The findings illustrate that the NeuroSpike network’s training becomes more effective and computationally less demanding due to the integration of spiking neurons and the proposed RFE-CatBoost feature selection technique.