利用机器学习算法预测太阳能温室室内温度:比较分析与实用方法

IF 5.7 Q1 AGRICULTURAL ENGINEERING
Wenhe Liu , Tao Han , Cong Wang , Feng Zhang , Zhanyang Xu
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引用次数: 0

摘要

本研究以位于辽宁省沈阳市沈阳农业大学实验基地的一个日光温室为研究对象,开发基于机器学习算法的多步温度预测模型。研究采用随机森林(RF)、多元线性回归(MLR)、支持向量回归(SVR)、长短期记忆递归神经网络(LSTM)和门控递归单元(GRU)五种算法进行温度预测。实验数据采集于日光温室内外气象站。这项研究的创新之处在于它对不同时间步长的温度预测进行了系统的评估。在15 ~ 1440 min范围内选择了21个预测区间,并对每个时间步采用K-fold交叉验证对5种预测模型的性能进行了评估。结果表明,GRU(门控循环单元)模型在所有21个预测范围内都优于所有其他算法,其中短期预测(15 min)的R²为0.991,长期预测(1440 min)的R²为0.992(如表1所示)。这一性能显著超过了LSTM、随机森林(RF)、支持向量回归(SVR)和多元线性回归(MLR),与LSTM相比,GRU在长期预测中将均方根误差(RMSE)降低了12.3% - 27.5%。RF和SVR模型在短期预测中表现出良好的性能,但随着预测范围的扩大,精度略有下降。MLR模型对短期预测(30分钟内)表现良好,但对较长时间步长表现不佳(R²<;0.9)。GRU的门控机制更简洁(只有更新门和复位门),与LSTM相比,不仅保证了高精度,而且显著提高了训练效率。本研究不仅比较了不同机器学习算法在太阳温室温度预测中的性能,还探讨了每种算法在不同预测范围内的适用性。研究结果为日光温室智能控制和精准管理提供了理论基础和技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach
This study focuses on a solar greenhouse located at the experimental base of Shenyang Agricultural University in Shenyang, Liaoning Province, to develop multi-step temperature prediction models based on machine learning algorithms. The research employs five algorithms: Random Forest (RF), Multiple Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory Recurrent Neural Network (LSTM), and Gated Recurrent Unit (GRU) for temperature prediction. Experimental data were collected from meteorological stations inside and outside the solar greenhouse. The innovative aspect of this study lied in its systematic evaluation of temperature predictions across various time steps. Twenty-one prediction horizons, ranging from 15 min to 1440 min, were selected and the performance of the five predictive models was assessed using K-fold cross-validation for each time step. Results demonstrated that the GRU (Gated Recurrent Unit) model outperformed all other algorithms across all 21 prediction horizons, with short-term prediction (15 min) achieving an R² of 0.991 and long-term prediction (1440 min) maintaining an R² of 0.992 (as shown in Table 1). This performance significantly exceeded that of LSTM, Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR), with GRU reducing the root mean squared error (RMSE) by 12.3 %–27.5 % compared to LSTM in long-term predictions. The RF and SVR models demonstrated good performance for short-term predictions, but showed slight accuracy degradation as the prediction horizon extended. The MLR model performed adequately for short-term predictions (within 30 min), but exhibited poor performance for longer time steps (R² < 0.9). GRU, by virtue of its more concise gating mechanism (featuring only update gates and reset gates), not only ensured high precision but also significantly improved training efficiency compared to LSTM. This research not only compared the performance of different machine learning algorithms in solar greenhouse temperature prediction but also explored the applicability of each algorithm across various prediction horizons. The findings provide a theoretical foundation and technical support for intelligent control and precise management of solar greenhouses.
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