Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n

Abstract In this study, a linear parametric autoregressive model with external inputs (ARX) and a neural network-based nonlinear autoregressive model with external inputs (NNARX) are developed to predict the thermal behaviour of an open office in a modern building. External and internal climate data recorded over three months were used to build and validate models for predicting dry bulb temperature and relative humidity for different time-scales (30 min to 3 h ahead). In order to compare the accuracy for different step-ahead predictions, different performance measures, such as goodness of fit, mean squared error, mean absolute error and coefficient of determination between predicted model output and real measurements, were calculated. For the NNARX model, the optimal network structure after training, is subsequently determined by pruning the fully connected network using the optimal brain surgeon strategy. The results demonstrate that both models provide reasonably good predictions but the nonlinear NNARX model outperforms the linear ARX model. These models can both potentially be used for improving indoor air quality by focusing on building intelligence into the controller in HVAC plants, in particular, adaptive control systems.