Thermal behaviour model identification for an office space using BMS data

In this study, linear Box—Jenkins, output-error and non-linear neural network autoregressive NARX models are investigated to predict the thermal behaviour of an office positioned in a modern commercial building. External and internal climate data recorded over a summer season were used to build and validate models. The paper exploits the potential of using linear and non-linear models to predict room temperature at different time scale ahead (5 min or 4 h ahead). The prediction performance is evaluated using the criteria of goodness of fit, errors and mean-squared error between predicted model output and real measurements. The results demonstrate that all models provide reasonably good predictions but non-linear models outperform linear models.Practical application: Prediction of room temperature by black-box linear and non-linear models obtained can be utilised in the building temperature control strategy. When there is any change in the building thermal behaviour (e.g. more equipments added or office eq...