ANN Based Learning to Kalman Filter Algorithm for Indoor Environment Prediction in Smart Greenhouse

In this article, we have proposed a learning to prediction based novel approach for improving the accuracy of prediction algorithms in dynamic conditions. The proposed model is composed of two modules, including the prediction module and the learning module. The learning module is responsible to regularly examine the prediction module and tune its performance by assessing its outcomes together with any other external parameters that can affect its performance. In order to determine the effectiveness of the proposed idea, a learning module based on the artificial neural network (ANN) is developed for improving the accuracy of the Kalman filter algorithm. Experimental investigations are conducted in a greenhouse indoor environment to accurately predict indoor climate parameters (temperature, <inline-formula> <tex-math notation="LaTeX">$CO_{2}$ </tex-math></inline-formula>, and humidity) from noisy sensors readings using the Kalman filter algorithm. Among the various components of the Kalman filter algorithm includes a fixed value of <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> (observation error covariance), which significantly degrades the performance of the Kalman filter algorithm in dynamic conditions. Greenhouse sensor readings are affected by changing external environmental conditions and internal greenhouse actuators operations. The amount of error in current readings is estimated using ANN-based learning modules to update the parameter <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> in the corresponding Kalman filter module. Performance evaluation of the proposed model based on learning is conducted in two different case studies using average and maximum based error models. Experimental results show that the prediction accuracy of the conventional Kalman filter is significantly improved by proposed learning to prediction scheme.

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