比例积分微分增强器用于基于神经网络的时间序列预测：以用水需求预测为例
Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
AI总结：
本文提出了一种基于PID控制的增强方法，用于提升神经网络在多步时间序列预测中的性能，通过案例研究验证了其在用水需求和能耗预测中的有效性。
摘要：
多步时间序列预测是决策者在多个工业领域中不可或缺的辅助步骤。近年来，利用神经网络组件的各种人工智能技术被频繁用于完成这一步骤。然而，神经网络结构的复杂性仍然成为预测精度的关键问题。本文研究了一种受比例-积分-微分（PID）控制方法启发的方法，旨在增强用于周期性时间序列信息多步预测的神经网络模型的性能，同时保持系统复杂性的影响极小。该PID方法应用于每个时间步的预测值，使该值更接近真实值。用水需求预测问题被作为案例研究，其中两种文献中的深度神经网络模型被用来证明所提出增强方法的有效性。此外，为了证明该PID增强器对其他类型周期性时间序列预测问题的适用性，它被应用于提高用于多步预测小时能耗的神经网络模型的准确性。原始预测模型结果与应用所提出技术后的结果比较，证明了所提出方法在预测精度和系统复杂性方面的优越性。
英文摘要：
Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional-integral-derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
