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.

翻译：短期负荷预测因其固有的非线性和动态特性，在电力系统的高效运行与规划中至关重要。深度学习领域的最新进展已展现出解决这一挑战的前景，然而这些方法常面临超参数敏感性、可解释性不透明以及实时部署计算开销高等问题。本文提出了一种能够克服上述障碍的新型解决方案。该方法利用粒子群优化算法自动探索并优化超参数，采用多头注意力机制识别对精确预测至关重要的显著特征，并通过精简框架提升计算效率。我们基于真实电力需求数据集对该方法进行了严格评估。结果凸显了其在准确性、鲁棒性和计算效率方面的优越性。值得注意的是，1.9376的平均绝对百分比误差标志着对现有最优方法的重大突破，开创了短期负荷预测的新纪元。