基于ISSA⁃ELM模型的温室环境参数预测研究

Research on Prediction of Environmental Parameters in the Greenhouse Based on ISSA⁃ELM Model

王瑶 张孟航 王伟 王进

Yao WANG Menghang ZHANG Wei WANG Jin WANG

河北工业大学 能源与环境工程学院，天津 300400

School of Energy and Environmental Engineering，Hebei University of Technology，Tianjin 300400，China

温室环境系统具有非线性、多变量和强耦合的特点，传统的温室模型难以预测其真实环境。采用极限学习机、BP神经网络和支持向量机三种模型对温室温度、湿度和光照强度进行了预测分析，结果显示极限学习机模型预测值与温室环境实时参数最为相近。为提高温室环境参数的预测精度，采用改进的麻雀搜索算法对极限学习机模型进行优化，预测的环境参数与天津某温室实测数据吻合较好，证实了所提出预测模型用于温室环境调控的可行性。

Traditional mechanism models of greenhouses are difficult to reflect the real greenhouse environment due to nonlinear, multivariate, and strongly coupled characteristics. In this paper, extreme learning machine (ELM), back propagation (BP) neural network, and support vector machine (SVM) are used to predict and analyze the temperature, humidity, and light intensity of the greenhouse. The results show that the predicted values of ELM model are the most similar to the real?time parameters of greenhouse environment. In order to further improve the prediction accuracy of environmental parameters in the greenhouse, the improved sparrow search algorithm (ISSA) is used to optimize ELM model in this paper. The predicted environmental parameters are in good agreement with the measured data of a greenhouse in Tianjin, which confirms the feasibility of the proposed prediction model for the control of greenhouse environment.

国家自然科学基金项目(52176067);河北省自然科学基金项目(E2021202163)

10.12422/j.issn.1672-6952.2024.04.010