Greenhouse Air Temperature and Humidity Prediction Based on Improved BP Neural Network and Genetic Algorithm

Abstract

The adequacy of improved back propagation (IBP) neural network to model the inside air temperature and humidity of a production greenhouse as a function of outside parameters including temperature, relative humidity, wind speed, and solar radiation was addressed. To avoid standard BP algorithm’s shortcoming of trapping to a local optimum and to take advantage of the genetic algorithm (GA)’s globe optimal searching, a new kind of hybrid algorithm was formed based on the IBP neural network and GA. BP neural network was improved by adding the inertia impulse and self-adaptation learning rate to lessen convergence vibration and increase the learning speed. Then the initialized weights and thresholds of IBP neural network were optimized with GA. Through carrying out the experiments, the specimen data were collected on half-hourly basis in a greenhouse. After the network structure and parameters were determined reasonably, the network was trained. A comparison was made between measured and predicted values of temperature and relative humidity, and the results showed that the IBP neural network model combined with GA given a good prediction for inside temperature and humidity. By using the root mean square error (RMSE) algorithm, the RMSE between temperature predicted and measured was 0.8°C, and the relative humidity RMSE was 1.1%, which can satisfy with the demand of greenhouse climate environment control.