Temperature prediction of solar greenhouse based on NARX regression neural network

Abstract

Temperature has an important influence on plant growth and development. In protected agriculture production, accurate prediction of temperature environment is of great significance. However, due to the time series, nonlinear and multi coupling characteristics of temperature, it is difficult to achieve accurate prediction. We proposed a method for building a solar greenhouse temperature prediction model based on a timeseries analysis, that considers the time series characteristics and dynamic temperature changes in the greenhouse system. The method would predict the temperature of greenhouse, and provide reference for the temperature change law in protected agriculture. A parameter analysis was performed on the nonlinear autoregressive exogenous (NARX) neural network, and a solar greenhouse temperature time series prediction model was established using the NARX regression neural network. The results showed that the proposed model depicted a maximum absolute error of 0.67 °C, and model correlation coefficient of 0.9996. Compared with the wavelet and BP neural networks, the NARX regression neural network accurately predicted and significantly outperformed in the solar greenhouse temperature prediction model. Moreover, the prediction model can accurately predict temperature trends within the solar greenhouse and is pivotal to obtaining precise control of solar greenhouse temperature.