Short-Term Load Forecasting using Hybrid Approach Combining African Vulture Optimization Algorithm and Self-Adaptive Neural Networks

In this paper, we present a hybrid approach that combines the African Vulture Optimization Algorithm (AVOA) with Self-Adaptive Neural Networks (SANNs) for short-term load forecasting. The AVOA, a metaheuristic inspired by the foraging behaviour of vultures, is utilised to optimise the parameters of a Self-Adaptive Neural Network, which adjusts its structure and learning parameters based on the problem at hand. Our experimental results, conducted on several benchmark datasets, demonstrate that the hybrid model significantly improves forecasting accuracy compared to conventional methods, offering a promising solution for more reliable and efficient load forecasting in power systems.