Verification and forecasting of temperature and humidity in solar greenhouse based on improved extreme learning machine algorithm

Solar greenhouse temperature and humidity models play an important role in its structure design and control. Solar greenhouses are multi-input and multi-output (MIMO) systems, and they are highly nonlinear and strongly coupled systems that are largely influenced by the outside weather (such as wind speed, outside temperature and humidity) and many other practical constraints (such as blowing and moistening cycle). Therefore, solar greenhouse temperature and humidity models are difficult to establish by mechanism analysis methods. Due to its ability to approximate complex nonlinear mapping directly from the input samples, neural network can provide models for many kinds of natural and artificial phenomena that are difficult to handle using classical parametric techniques. Among many kinds of neural networks, extreme learning machine (ELM) for single-hidden layer feed forward neural networks has been studied more thoroughly. But there are limitations existing in ELM such as fixed hidden-layer activation function and overfitting when minimizing training error. In order to achieve comprehensive control of temperature and humidity in the solar greenhouse and improve prediction accuracy, an improved ELM based on orthonormal basis function is proposed in the paper. First, it determines the number of the nodes in hidden layer by using empirical mode decomposition (EMD); second, on the basis of statistical learning theory combined with the empirical risk and structural risk, it takes minimal value of the sum of the minimum output weight and the minimum error; third, it identifies the greenhouse microclimate environmental factors. The prediction model of temperature and humidity is established by the improved ELM. The proposed method is tested in the solar greenhouse of Vegetable and Fruit Research Institution of Chinese Academy of Agricultural Sciences, which is located in 40°07'N, 116°09'E. According to the characteristics of solar greenhouse environment, the inputs of ELM are temperature and humidity outside solar greenhouse, light and wind speed, and the outputs of ELM are temperature and humidity inside solar greenhouse. Root mean square error and model validity are used as index to measure the generalization ability and the accuracy of model. According to the results of EMD for signal of temperature and humidity inside solar greenhouse, the number of nodes for the traditional ELM and the improved ELM is 9. This paper adopts sigmoidal function as the excitation function of the traditional ELM. The improved ELM is based on orthonormal basis function, and its excitation function coefficient of nerve cells in hidden layer is 30 and 10, respectively. Compared to the traditional ELM, the prediction results of the improved ELM show that temperature error and humidity error are reduced by 2℃ and 5% respectively, root mean square error of temperature is reduced by 0.4758℃ and that of humidity is reduced by 0.6857 percent, and model validity of temperature and humidity are improved by 0.0384 and 0.0314 respectively, so the improved ELM is effective, and it has certain reference value for intelligent control of the solar greenhouse microclimate.