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：精准的负荷预测在众多领域发挥着至关重要的作用，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征。为此，时间序列模型（ARIMA）与深度学习模型（ANN、LSTM、GRU等）被广泛采用且通常能取得较高的预测效果。本文分析了近期提出的基于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能够学习长程依赖关系，因此具有提升负荷预测性能的潜力。我们应用差分进化等多种元启发式算法，寻找基于Transformer的神经网络模型的最优超参数，以实现精准预测。差分进化算法能够为不可微、多目标或约束优化问题提供具有可扩展性、鲁棒性的全局最优解。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，对比了集成不同元启发式算法的Transformer神经网络模型在负荷预测中的性能表现。研究结果表明，经元启发式算法优化的Transformer神经网络模型在提升负荷预测精度方面具有显著潜力，并为每个模型提供了最优超参数。