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：准确的负荷预测在众多领域至关重要，但传统统计模型难以精确捕捉动态电力系统的复杂特性。为此，时间序列模型（ARIMA）和深度学习模型（ANN、LSTM、GRU等）被广泛采用并常取得较好效果。本文分析了近年来开发的基于Transformer的神经网络模型在负荷预测中的有效性。Transformer模型因其注意力机制能够学习长距离依赖关系，具有提升负荷预测性能的潜力。我们应用多种元启发式算法（包括差分进化）来寻找基于Transformer的神经网络的最优超参数，以实现准确预测。差分进化为不可微、多目标或约束优化问题提供了可扩展、鲁棒的全局解决方案。本研究基于均方误差（MSE）和平均绝对百分比误差（MAPE）等数值指标，将所提出的基于Transformer的神经网络模型与不同元启发式算法集成，比较其在负荷预测中的性能。研究结果证明了元启发式增强型Transformer神经网络模型在提升负荷预测精度方面的潜力，并为各模型提供了最优超参数。