A dynamic model that describes the cooling coils’ thermal qualities is developed and solved using the method of classical control theory. The dynamic relationships are obtained between the coil’s heat exchange and its five important parameters that include inlet air temperature, inlet air humidity, inlet water temperature, airflow rate and water flow rate. Experiments have been done to validate the model. Afterwards, simulations are made by the model to analyze the dynamic heat exchange of coils when some perturbations occur under different initial conditions. Then, some useful conclusions are drawn based on the analysis.

A large cooling system of residential building in Changsha City in China was investigated in the summer of 2003. The relationships among the controlled variables, uncontrolled variables and the chillers’ performance were obtained empirically with the test data. A model of optimal operation for the system was established based on these empirical relationships. A parameter, system coefficient of performance (SCOP), is presented to analyze the effect of energy saving of the cooling system. The results show that the energy saving is likely to reach as high as 10% by applying the optimal model to the cooling system.

Energy cost allocation systems apportion cooling and/or heating costs among the individual apartments in centrally metered buildings based on various methods of energy metering. The most commonly used systems measure one or more parameters related to the thermal output of the terminal element but do not measure enough parameters to provide an accurate energy measurement because of many reasons, such as investment, installation, maintenance cost, etc. They typically assume that cooling or heating output is linearly proportional to a quantity, which may be a single temperature, a difference between two temperatures, or an average of two temperatures. Most of them only perform well over a certain limited range of application conditions (Hewett 1994). To solve this problem, this paper puts forward a new method called “cooling/heating metering on the air side,” which uses five parameters, including indoor air temperature and humidity, entering water temperature, airflow rate, and water flow rate, to calculate the thermal output of the terminal elements. These needed parameters can be obtained by a direct or indirect way, which is discussed at length in this paper. Theoretic analysis shows that the metering errors of the method are lower than 25% in most cases and will be further reduced when some measurements are made in the actual situation. The new method is comparatively economical and efficient in air-conditioning thermal metering.

Heating/cooling cost allocation is of growing concern in many countries because it contributes much to the energy conservation of central air-conditioning system. This paper intends to summarize the current studies and techniques on the heating/cooling cost allocation and discuss about the key problems to them. The contents mainly include the following parts: (1) probing into the methods of heating/cooling measurement; (2) establishing the calculation model for thermal billing; (3) presenting several typical systems for heating/cooling cost allocation. The significance of the paper is to help the engineers in this field have a good idea of the current development of heating/cooling cost allocation and promote the application of the relevant technologies to practice.

Predicting the next-24-hour load in a building is essential for the optimal control of heating, ventilating and air-conditioning (HVAC) systems that use thermal/cool storage technology and also for cost and energy reduction of the non-storage systems. To fully integrate the advantages of several models and improve the accuracy of forecasting load, the application of the combined forecasting method to hourly load forecasting is presented in this paper. The method of Analytic Hierarchy Process (AHP) is employed to deduce the weights of each model. A case study shows that the combined forecasting model based on AHP may be better than the individual ones in predicting the building’s hourly load for the future hours.