Recurrent Neural Network Model for Prediction of Microclimate in Solar Greenhouse

A recurrent neural network(RNN) based dynamic back propagation(BP) algorithm model with historical internal inputs are developed to predict the temperature and humidity of a solar greenhouse in the north of China. Climate data including air and substrate temperature, air humidity, illumination and C0 2 concentration recorded over eight days were used to build and validate models for climatic prediction. In order to compare the accuracy of predictions, different performance measures, such as average relative error (ARE), mean absolute error (AME) and root mean square error (RSME), were calculated, for using BP and untrained RNN neural networks using the same processing. For the RNN model, a context layer as the last hidden layer output, is input with the next input to the next hidden layer, which is equivalent to the state feedback. The results demonstrate that the RNN-BP model provides reasonably good predictions with the RSME for temperature 0.751 and 0.781 for humidity, including both of the R 2 is both above 0.9, which outperforms the compared models tested in this paper.