Development of room temperature and relative humidity linear parametric models for an open office using BMS data

Abstract This study investigates Box–Jenkins (BJ), autoregressive with external inputs (ARX), autoregressive moving average with external inputs (ARMAX) and output error (OE) models to identify the thermal behaviour of an office positioned in a modern commercial building in London. These models can all be potentially used for improving the performance of the thermal environment control system. External and internal climate data, recorded over the summer, autumn and winter seasons, were used to build and validate the models. The paper demonstrates the potential of using linear parametric models to predict room temperature and relative humidity for different time scales (30 min or 2 h ahead). The prediction performance is evaluated using the criteria of goodness of fit, coefficient of determination, mean absolute error and mean squared error between predicted model output and real measurements. The results demonstrate that all models provide reasonably good predictions but the BJ model outperforms the ARMAX and ARX models.