Abstract

A heating system integrated into the building envelope, such as a floor radiant heating system, is defined as a building envelope heating system (BEHS). Thermal mass usually integrated with such a heating system can be utilized to lower both peak loads and operating costs and to reduce room temperature swings by predictive control while utilizing solar gains to reduce energy consumption. Nevertheless, techniques required for the predictive control of BEHSs need to be developed in order to materialize these potential benefits. A methodology proposed in this study integrates building thermal analysis and predictive control of BEHS. A computer method for generating the symbolic transfer function of buildings is developed as the first part of the methodology. It includes hybrid signal flowgraph and generalized-nodal admittance formulations, and an algebraic algorithm associated with the constraint conditions of inequalities. New concepts for thermal network modelling are presented, with which a combined thermal parameter such as the operative temperature can be explicitly represented with an imaginary thermal network. A building thermal system is systematically modelled with a generalized thermal network. Because some design parameters of interest, such as amount of thermal mass, can be kept as symbols in the model, sensitivity analysis, optimum design and control studies of building systems can be significantly facilitated. An optimal predictive control system developed integrates a weather predictor, set-point optimizer, a system identifier and an adaptive Generalized Predictive Control algorithm so as to achieve high building thermal performance. A new weather predictor, simplified through normalization, makes it feasible to quantify the qualitative weather forecast for solar radiation. Several implementation issues in on-line parameter estimation are investigated through experiments in an outdoor passive solar test-room. A Generalized Predictive Controller (GPC) with a feedforward control scheme is improved with a new algorithm. The zone set-point is optimized through the combination of dynamic programming and on-line simulation. The methodology has been verified with both experiments and simulations. Results show that the weather predictor is capable of generating reasonably accurate solar radiation and outdoor temperature profiles for one day. A building thermal model can be robustly identified under the supervision rules. The performance of GPC is superior to conventional on-off and PI controllers. The optimal set-point can be efficiently generated by the proposed approach, which may lead to large savings in operating energy costs when a BEHS is properly designed and operated.