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Signifirant progress has been made in recent years on the development of modular and generic simulation programs for investigating the thermal behaviour of buildings and associated HVAC plant and controls. However, many of these programs are inflexible for the specific analysis of HVAC plant and control systems over short time scales. The nature of this inflexibility is discussed and a remedy is sought through the development of a low-order lumped-capacity thermal model of a building space. This is expressed as a linear time-invariant state-space description. A non-linear dynamic model of a hot water heating system with feedback control has been added and results under open-loop conditions are presented. The model has the advantage of simplicity and computational efficiency. Results are compared with field-monitored data obtained from a building in use and an excellent agreement between the two is demonstrated, though this comparison is restricted to a northfacing building space with high thermal capacity. List of symbols Aa Emitter external area (m2) (M2) Atcross Emitter cross-sectional area (m2) AtouJ Overall area of construction elements (ml) AW Emitter internal area (ml) c Clearness index C1 Thermal capacity of the first layer (J K-1 M-2) C6 Thermal capacity of the indoor air (J K-1) q. Thermal capacity of the ith room element Q K-') Ck Thermal capacity up to the kth layer (J K-1 m-1) C. Thermal capacity of emitter material (J K-') cri Specific heat capacity of the Ith layer (J kg-1 K-1) cpw Specific heat capacity of water (J kg-I K-1)Cto~~ Overall thermal capacity U K-1) C Thermal capacity of emitter water (J K-') d¡Tube internal diameter (m) Edilf Diffuse solar radiation (W m-1) Edjr Direct solar radiation (W m-1) Ect Global extraterrestrial radiation (W m~2) Eboriz Global terrestrial solar radiation (W m-1)

Most heating, ventilation and air conditioning (HVAC) control systems are considered as temperature control problems. In this work, the predicted mean vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. This is achieved through the use of a fuzzy logic controller that takes into account a range of human comfort criteria in the formulation of the control action that should be applied to the heating system to bring the space to comfort conditions. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. Simulation results show that the proposed control strategy makes it possible to maximize the indoor thermal comfort and, correspondingly, a reduction in energy use of 20% was obtained for a typical 7-day winter period when compared with conventional control.

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