## 1 Introduction
[PERSON] et al. [24] developed a DL approach using LSTM for Ficus growth, tomato yield prediction in greenhouse environment, achieving high prediction accuracy. [PERSON] et al. [25] used LSTM, RNNs and ANNs for greenhouse air temperature forecasting. [PERSON] et al. [26] proposed a model using LSTM to predict the indoor temperature, humidity, light, carbon dioxide concentration, soil temperature and soil humidity. Considering the most influential environmental parameters, including soil moisture, air temperature and humidity, carbon dioxide concentration and light intensity, [PERSON] et al. [27] developed a BO-DNN model for classification of the rose yield environment. In order to reduce time complexity, [PERSON] et al. [28] developed a model using environmental information of adjacent areas. Although there have been many studies on the environmental parameters of solar greenhouses, the models of these studies are difficult to apply to the actual solar greenhouse. Because the internal space of a solar greenhouse is small, the changes in its internal environmental parameters are gradual. Therefore, the cost of environmental parameter sensor inside the greenhouse is low, and the monitoring accuracy is higher. In contrast, the outdoor environmental parameters of solar greenhouses are usually monitored by small weather stations, the monitoring coverage of weather stations is relatively small, and the outdoor environment of the solar greenhouse is complex and changeable, and the accuracy of environmental parameters collected by small weather stations is relatively poor. Although there have been many related studies on solar greenhouses, due to the lack of reliable data from small-scale meteorological stations and the output of numerical weather models on these time scales, it is very difficult to accurately predict the greenhouse environmental parameters in the short term (1-24 h). Indeed, the correctness of the reference evapotranspiration (ET\({}_{\text{o}}\)) forecast also remains a challenging computational task. The solar greenhouse is a non-linear and dynamic system, in order to adjust the greenhouse environmental parameters accurately and timely, we need to predict the indoor environmental parameters in advance. Deep learning models are usually used to predict nonlinear and dynamic system, these models can also be applied to the prediction of greenhouse environmental parameters. In this study, three deep learning models (DNNR, LSTM, and CNN-LSTM) were constructed to simulate and predict the indoor environmental parameters of two typical greenhouses. The architecture and hyperparameters are the main factors affecting the prediction accuracy of the deep learning models. It is essential to optimize the architecture and hyperparameters of those models. Grid search is one of the most commonly applied methods to optimize the hyperparameters. However, when the dimension of the hyperparameter is large, the time cost is very high. Some scholars have used an orthogonal design method to optimize the hyperparameters of the deep learning model. In the study, the orthogonal design was also used to optimize the important hyperparameters of three models. Therefore, considering the short-term change in outdoor parameters have great influence on the indoor environmental parameters, the models were trained and tested at four forecast time points, and at the same time, the minimum amount of necessary data was also determined. Finally, a comparison was made between ET\({}_{\text{o}}\) computed with the predicted environmental parameters and the actual ET\({}_{\text{o}}\). In general, in order to predict short-term indoor environmental parameters and crop water consumption of solar greenhouse, this paper optimizes three deep learning prediction models through orthogonal experimental design to obtain the best prediction model. Compared with the reported related studies, the main novelties of this study are highlighted below.