Research on solar greenhouse temperature and humidity prediction model based on SSA-Elman

doi:10.13733/j.jcam.issn.2095‑5553.2024.11.011

Abstract

Abstract: It is very important for effective acquisition of temperature and humidity change trends in solar greenhouse to realize the accurate control of greenhouse environment. In order to effectively improve the prediction accuracy and reliability of temperature and humidity in sunlight greenhouses, a greenhouse temperature and humidity environmental prediction model of optimized Elman neural network based on the Sparrow Search Algorithm (SSA) is proposed. The study utilizes the main environmental influencing factors selected through Spearman correlation analysis as input variables, with future temperature and humidity inside the sunlight greenhouse as output variables. The Sparrow Search Algorithm is adopted to optimize and adjust the parameters of the Elman neural network model, thus completing the prediction of temperature and humidity trends in the sunlight greenhouse. Experimental validation is conducted by using monitoring data from a winter facility tomato sunlight greenhouse in Shandong Province from October 1, 2022, to January 1, 2023. The results show that the root mean square error, mean absolute error and coefficient of determination of the SSA-Elman model are 0.592, 0.320 and 0.963 for temperature prediction, and 0.120, 2.530 and 0.972, respectively, for humidity prediction, indicating that the proposed model can effectively make an accurate prediction of the temperature and humidity of the solar greenhouse. The proposed model can effectively predict the temperature and humidity of solar greenhouses, which can provide reliable data support and a decision‑making basis for the future accurate regulation of the greenhouse environment.

Key words: Elman neural network, greenhouse temperature and humidity, agriculture Internet of Things, Sparrow Search Algorithm

摘要： 有效获取日光温室的温湿度变化趋势对实现温室环境精准调控至关重要。为提高日光温室温度和湿度的预测精度和可靠性，提出一种基于麻雀搜索算法(SSA)优化Elman神经网络的温室温湿度环境预测模型。研究采用斯皮尔曼相关性分析方法筛选出主要的环境影响因子作为输入变量，以日光温室内未来的温度和湿度分别作为输出变量，利用麻雀搜索优化算法对Elman神经网络模型参数分别进行优化调整，完成对日光温室的温湿度变化趋势预测。以山东地区2022年10月1日—2023年1月1日的冬季设施番茄日光温室的监测数据进行试验验证。结果表明，SSA-Elman模型对温度的预测指标均方根误差、平均绝对误差和决定系数分别为0.592、0.320和0.963；对湿度的预测指标均方根误差、平均绝对误差和决定系数分别为0.120、2.530和0.972，说明所提出的模型可有效用于对日光温室温湿度进行精准预测，可为未来温室环境的精准调控提供可靠的数据支撑和决策依据。

关键词: Elman神经网络, 温室温湿度, 农业物联网, 麻雀搜索算法

CLC Number:

S625
TP183

Pan Jigang, , , Liu Pingzeng, , , Zhang Yan, , , Zhang Mingzhi, , , Liu Chuanlong, , . Research on solar greenhouse temperature and humidity prediction model based on SSA-Elman[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(11): 69-76.

潘纪港, 柳平增, 张艳, 张铭志, 刘传龙, . 基于SSA-Elman的日光温室温湿度预测模型的研究[J]. 中国农机化学报, 2024, 45(11): 69-76.

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