  Short-term load forecasting is of paramount importance in the efficient
operation and planning of power systems, given its inherent non-linear and
dynamic nature. Recent strides in deep learning have shown promise in
addressing this challenge. However, these methods often grapple with
hyperparameter sensitivity, opaqueness in interpretability, and high
computational overhead for real-time deployment. In this paper, I propose a
novel solution that surmounts these obstacles. Our approach harnesses the power
of the Particle-Swarm Optimization algorithm to autonomously explore and
optimize hyperparameters, a Multi-Head Attention mechanism to discern the
salient features crucial for accurate forecasting, and a streamlined framework
for computational efficiency. Our method undergoes rigorous evaluation using a
genuine electricity demand dataset. The results underscore its superiority in
terms of accuracy, robustness, and computational efficiency. Notably, our Mean
Absolute Percentage Error of 1.9376 marks a significant advancement over
existing state-of-the-art approaches, heralding a new era in short-term load
forecasting.
