Multiple linear regression based model for the indoor temperature of mobile containers.

It is important to work out precise and easy-to-use mathematical models to predict the indoor temperature in buildings for human residence. Such models can support model-based/predictive controls to efficiently maintain the temperature at a comfortable level. Accordingly, as main contribution, the paper proposes a new, easy-to-use, black-box type, multiple linear regression (MLR) based model for the indoor temperature of mobile (office) containers. The model, having low computational demand, could be easily generalized for different types of residence places (in the future). A discretized, physically-based model version of the classical, widely used heat transfer theory (based on energy balance) is recalled for comparison with the MLR-based model. Both models have the same (exogenous) inputs: global solar irradiance, environment temperature and wind speed. Both models are validated based on measured data. The MLR-based model is more precise, its modelling error is 7.1%, which means that it can be used well for general engineering aims. Moreover, the model is detailed in time (it gives an output value per half minute), so it could be properly used for real-time prediction and control purposes. Another contribution of the paper is that the MLR-based model is used to estimate the application potential of solar collectors, installed on the top of the container, for space heating. Based on the results, two solar collectors could extend the time with comfortable indoor temperature by more than 5 h within a three-day period in spring (in Hungary). Finally, conclusions and possible topics for future researches are provided.