Good models for building thermal behaviour are an important part of developing building energy management systems that are capable of reducing energy consumption for space heating through model predictive control. A popular approach to modelling the temperature variations of buildings is grey-box models based on lumped parameter thermal networks. By creating simplified models and calibrating their parameters from measurement data, the resulting model is both accurate and shows good generalisation capabilities. Often, parameters of such models are assumed to be a combination of different physical attributes of the building, hence they have some physical interpretation. In this paper, we investigate the dispersion of parameter estimates by use of randomisation. We show that there is significant dispersion in the parameter estimates when using randomised initial conditions for a numerical optimisation algorithm. Further, we claim that in order to assign a physical interpretation to grey-box model parameters, we require the estimated parameters to converge independently of the initial conditions and different datasets. Despite the dispersion of estimated parameters, the prediction capability of calibrated grey-box models is demonstrated by validating the models on independent data. This shows that the models are usable in a model predictive control system.

The paper refers to the development of a continuous time mathematical heating model for a building unit based on the first principles. The model is described in terms of the state space variables, and a lumped parameter approach is used to represent the room air temperature and air density using mass and energy balances. The one-dimensional heat equation in cartesian coordinates and spherical coordinates is discretized in order to describe the thermic characteristics of the layers of the building framework and furniture respectively. The developed model is implemented in a MATLAB environment, and mainly a theoretical approach is used to validate it for a residential building unit. Model is also validated using experimental data for a limited period. Short term simulations are used to test the energy efficiency of the building unit with regard to factors such as the operation of heat sources, ventilation, occupancy patterns of people, weather conditions, features of the building structure and heat recovery. The results are consistent and are obtained considerably fast, implying that the model can be used further in modelling the heating dynamics of complex architectural designs and in control applications.

Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported.

This paper presents a comparison of different scenarios in controlling the space heating systems in residential buildings. The space heating energy consumption of a three-storey residential building is estimated using traditional control methods (fixed-temperature schedule and fixed-time schedule) and a mathematical model-based control strategy. The model-based control technique takes the usage pattern of the building into account and operates the heaters based on the calculated heating time of the building. The results from the experiments confirm that the use of a model in heating control is the best option, which can save up to 1400 kWh and 320 kWh per year compared to a fixed-temperature schedule and fixed-time schedule, respectively.

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