Abstract
Short-term load forecasting plays an essential role in the efficient management of electrical systems. Building an optimization model that will enhance forecasting accuracy is a challenging task and a concern for electrical load prediction. Due to Artificial Neural Networks (ANNs), the final result depends on initial random weights and thresholds that affect the stability of the forecast. Although much devotion is being given to improving the forecast accuracy, convergence, complexity, and resilience need to be considered for stable predictive models. To overcome this limitation, this work has jointly considered the Wavelet Neural Network (WNN) and Self-Adaptive Momentum Factor (SAMF) to achieve fast convergence, stability, and high accuracy. The proposed hybrid model is developed by combining the Feature Engineering (FE) and SAMF with the WNN model. The FE removes the irrelevant data and shallow features to ensure high computational performance. In contrast, the SAMF combines the wavelet transform's time and frequency domain properties and adjusts the WNN model's corresponding parameters. This ensures the global optimum solution while returning accurate predictive results. Finally, the SAMF is used to tune the control parameters of WNN by initializing the random weights and thresholds to accelerate the convergence rate and improve the accuracy compared to the Back-Propagation (BP) method. The proposed hybrid model is tested on the real-time datasets taken from the Australian states of (New South Wales (NSW), and Victoria (VIC)). Experimental results show that the developed model outperforms other benchmark models such as WNN-IGA, BPNN, WNN-AMBA, and Enhanced WNN in terms of instability, rate of convergence, and accuracy.