![]() ![]() The dimension of the input vector of the model is reduced PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. First, PCA is used to screen the key influencing factors of the sheep house temperature. Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. Aiming at the randomness and empirical problem of parameter selection of the traditional single extreme Gradient boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). ![]() Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. ![]() For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. The best model of three type models at four prediction time points were selected, respectively. The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. The type of single-slope solar greenhouse is mainly used for vegetable production in China. ![]()
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