MPMS-SGH：面向日光温室的多参数多步预测模型

MPMS-SGH: Multi-parameter Multi-step Prediction Model for Solar Greenhouse

摘要:

准确预测日光温室的环境参数对实现精准环境调控至关重要。在日光温室中，温度、相对湿度和光照强度是关键环境参数。利用监测平台采集了日光温室内1年环境数据，包括温度、相对湿度和光照强度，以及同时段的室外温度、室外湿度和室外光照强度等气象数据。通过深入分析，研究了这些参数的特征及相互关系。分析表明，日光温室环境参数具有时序性、非线性和周期性特征，且存在复杂耦合关系，且在不同季节存在着显著性差异。 本研究提出日光温室多参数多步预测模型（MPMS-SGH），用于精确预测3种关键温室环境参数，且具有一定季节适应性。MPMS-SGH包括输入层、预处理层、特征提取层和预测层。输入层用于生成包含室内温度、室内湿度、室内光照强度以及室外温度和室外光照强度的原始序列矩阵；预处理层对原始序列矩阵进行归一化、分解和位置编码；特征提取层分别使用时序注意力机制和频域注意力机制从趋势分量和季节分量中提取特征；预测层利用多层感知机对室内环境参数（温度、相对湿度和光照强度）进行多步预测。参数选择实验评估了在不同输入输出序列长度下MPMS-SGH的预测性能。结果表明：当输出序列长度固定时，MPMS-SGH预测精度随输入序列长度增加呈先升后降趋势，输入序列长度为100时预测精度最高，此时温度、相对湿度和光照强度RMSE分别为0.22℃、0.28%和250lx；当输入序列长度固定时，随着输出序列长度增加，模型对3种环境参数的预测精度持续下降，输出序列长度超过45时MPMS-SGH预测精度显著降低。综合考虑模型的规模与性能，将MPMS-SGH的输入序列长度设为100，输出序列长度设为35。通过与SVR、STL-SVR、LSTM和STL-LSTM 4种预测模型对比实验表明，MPMS-SGH性能最优，其温度、相对湿度和光照强度RMSE分别达到0.15℃、0.38%和260lx，且序列分解有助于提升预测性能。进一步测试MPMS-SGH在不同季节的预测精度发现：该模型对室内温度预测精度最高，对相对湿度预测精度最低，且预测精度随季节波动。其中，春季晴天温室温度预测精度最高（R2=0.91），冬季晴天相对湿度预测精度最高（R2=0.83），秋季阴天光照强度预测精度最高（R2=0.89）；夏季晴天3种环境参数预测精度均为最低。

Abstract:

Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control. In solar greenhouses, temperature, humidity, and light intensity are crucial environmental parameters. The monitoring platform collected data on the internal environment of the solar greenhouse for one year, including temperature, humidity, and light intensity. Additionally, meteorological data, comprising outdoor temperature, outdoor humidity, and outdoor light intensity, was gathered during the same time frame. The characteristics and interrelationships among these parameters were investigated by a thorough analysis. The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability, non-linearity, and periodicity. These parameters exhibited complex coupling relationships. Notably, these characteristics and coupling relationships exhibited pronounced seasonal variations. The multi-parameter multi-step prediction model for solar greenhouse (MPMS-SGH) was introduced, aiming to accurately predict three key greenhouse environmental parameters, and the model had certain seasonal adaptability. MPMS-SGH was structured with multiple layers, including an input layer, a preprocessing layer, a feature extraction layer, and a prediction layer. The input layer was used to generate the original sequence matrix, which included indoor temperature, indoor humidity, indoor light intensity, as well as outdoor temperature and outdoor light intensity. Then the preprocessing layer normalized, decomposed, and positionally encoded the original sequence matrix. In the feature extraction layer, the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component, respectively. Finally, the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters (i.e. temperature, humidity, and light intensity). The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths. The results indicated that with a constant output sequence length, the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length. Specifically, when the input sequence length was 100, MPMS-SGH had the highest prediction accuracy, with RMSE of 0.22℃, 0.28%, and 250lx for temperature, humidity, and light intensity, respectively. When the length of the input sequence remained constant, as the length of the output sequence increased, the accuracy of the model in predicting the three environmental parameters was continuously decreased. When the length of the output sequence exceeded 45, the prediction accuracy of MPMS-SGH was significantly decreased. In order to achieve the best balance between model size and performance, the input sequence length of MPMS-SGH was set to be 100, while the output sequence length was set to be 35. To assess MPMS-SGH’s performance, comparative experiments with four prediction models were conducted: SVR, STL-SVR, LSTM, and STL-LSTM. The results demonstrated that MPMS-SGH surpassed all other models, achieving RMSE of 0.15℃ for temperature, 0.38% for humidity, and 260lx for light intensity. Additionally, sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance. To further evaluate MPMS-SGH’s capabilities, its prediction accuracy was tested across different seasons for greenhouse environmental parameters. MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity. And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons. MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring (R2=0.91), the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter (R2=0.83), and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm (R2=0.89). MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.