基于WOA-TCN的太阳辐照度预测算法
天津商业大学, 信息工程学院, 天津 300134
| 摘 要： | 太阳辐照度预测在多能协同供热系统的优化运行中扮演着关键角色。太阳辐照度作为影响供热系统效率的关键因素，其预测的准确性直接关系到供热系统的运行效率和能源消耗。然而，太阳辐照度具有时空间上的间歇性和非线性等不确定特性给现有的机器学习预测算法带来了挑战，特别是传统超参数优化策略较为简单使其难以满足优化调控需求。针对该问题，本文提出了一种基于鲸鱼算法优化时间卷积网络（Whale Algorithm Optimization Temporal Convolutional Network, WOA-TCN）的太阳辐照度预测模型。该模型充分利用了TCN时间卷积网络的并行处理能力与递归神经网络的时序建模功能，通过模拟鲸鱼行为的群智能优化算法来优化TCN的超参数，从而提高了模型的预测精度，使得太阳辐照度的时空演变规律能够更准确地捕捉，还能为多能协同供热系统的优化运行提供有力的技术支持。并通过实验对比了CNN、RNN等算法的预测指标，结果表明了基于WOA-TCN太阳辐照度预测模型的预测精度更高。 |
| 关 键 词： | 太阳辐照度预测; 鲸鱼优化算法; 时间卷积网络; 深度学习 |
| DOI： | 10.57237/j.jest.2024.04.001 |
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收稿日期: 2024-06-21
接受日期: 2024-07-13
在线出版日期: 2024-07-18
Solar Irradiance Prediction Algorithm Based on WOA-TCN
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
| Abstract: | The prediction of solar irradiance plays a crucial role in the optimization operation of multi energy collaborative heating systems. As a key factor affecting the efficiency of the heating system, the accuracy of solar irradiance prediction is directly related to the operational efficiency and energy consumption of the heating system. However, solar irradiance has uncertain characteristics such as intermittency and nonlinearity in time and space, which poses challenges for existing machine learning prediction algorithms in hyperparameter optimization and makes it difficult to meet optimization and regulation requirements. To address this issue, we propose a solar irradiance prediction model based on Whale Algorithm Optimization Time Convolutional Network (WOA-TCN). This model fully utilizes the parallel processing capability of TCN time convolutional networks and the temporal modeling function of recurrent neural networks. The hyperparameters of the TCN are optimized by a swarm intelligence optimization algorithm that simulates the behavior of whales, which improves the prediction accuracy of the model, enables the temporal and spatial evolution patterns of solar irradiance to be captured more accurately, and also provides strong technical support for the optimal operation of the multi-energy cooperative heating system. The prediction indexes of CNN, RNN and other algorithms are also compared through experiments, and the results show that the prediction accuracy of the solar irradiance prediction model based on WOA-TCN is higher. |
| Keywords: | Solar Irradiance Prediction; Whale Optimization Algorithm; Time Convolutional Network; Deep Learning |
基金
| 1. | 大学生创新创业训练计划资助项目 (202310069029) |
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