Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper, we analyze the efficacy of the recently developed Transformer-based Neural Network model in Load forecasting. Transformer models have the potential to improve Load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts. Differential Evolution provides scalable, robust, global solutions to non-differentiable, multi-objective, or constrained optimization problems. Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in Load forecasting accuracy and provide optimal hyperparameters for each model.

翻译：精确的负荷预测在多个领域至关重要，但传统统计模型难以准确捕捉动态电力系统的复杂动态特性。为此，时间序列模型（ARIMA）和深度学习模型（如ANN、LSTM、GRU等）被广泛采用并取得了较高的成功率。本文分析了近期发展的基于Transformer的神经网络模型在负荷预测中的有效性。Transformer模型凭借其注意力机制学习长程依赖关系的能力，具有提升负荷预测性能的潜力。我们应用差分进化等元启发式算法，为基于Transformer的神经网络寻找最优超参数，以生成精确的预测。差分进化算法能够为非可微、多目标或约束优化问题提供可扩展、稳健的全局解。本研究将所提出的基于Transformer的神经网络模型与不同元启发式算法进行集成，并基于均方误差（MSE）和平均绝对百分比误差（MAPE）等数值指标，比较它们在负荷预测中的性能。研究结果表明，元启发式算法增强的基于Transformer的神经网络模型在负荷预测精度方面具有潜力，并为每种模型提供了最优超参数。