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室内环境研究基于OTDBO-TCN-GRU算法的猪舍预测

Zhaodong Guo¹, Zhe Yin¹, Yangcheng Lyu¹

¹College of Software, Shanxi Agricultural University, Jinzhong 030801, China.

Animals : an open access journal from MDPI

|March 28, 2024

概括

准确预测猪舍环境因素如温度和湿度对于动物健康至关重要. 一个新的OTDBO-TCN-GRU模型显著提高了预测准确性,有助于对猪进行更好的环境控制.

科学领域:

农业工程农业工程
环境监测环境监测
动物科学动物科学

背景情况:

猪舍中的关键环境因素包括温度,湿度,氨和硫化.
准确预测这些变量对于优化猪生长,健康和福祉至关重要.
现有的预测模型的准确性很低,环境波动很大.

研究的目的:

开发一种新的混合模型,用于准确预测和优化猪舍中的环境因素.
在预测准确性和稳定性方面解决当前预测方法的局限性.

主要方法:

提出了一种混合模型:优化虫算法 (DBO) 与 Osprey Eagle 优化 (OOA),时间卷积网络 (TCN) 和门式循环单元 (GRU) 称为 OTDBO-TCN-GRU.
该OOA增强了DBO的全球搜索能力.
该模型使用DBO进行初始时间序列拟合,并使用TCN/GRU预测余值,捕捉长期依赖和非线性动态.

主要成果:

在预测氨度方面,OTDBO-TCN-GRU模型表现出色,MAE为0.0474,MSE为0.0039,R2为0.9871.
与DBO-TCN-GRU相比,观察到显著的改善 (MAE减少37.2%,MSE减少66.7%) 和OOA (MAE减少48.7%,MSE减少74.2%).
该模型对环境气体的预测误差小于0.3毫克/立方米,并且显示了对环境突然变化的稳定性.

结论:

OTDBO-TCN-GRU模型显著提高了猪舍中的环境因素时间序列的预测性能.
该模型为精确的环境控制提供了强大的决策支持,促进了猪福利和发展的最佳条件.
这种方法为密集畜牧业的关键环境参数管理提供了更具适应性和准确性的解决方案.

关键词:

在这里,GRU GRU GRU TCN TCN 是一个数字.虫优化算法虫优化算法环境预测模型的环境预测模型智能优化算法智能优化算法猪仓猪仓猪仓猪仓猪仓传感器传感器传感器传感器

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