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.

his paper mainly concerned about the dynamic response model of wet cooling coils that is developed by the Laplace transform method. The theoretic equations are firstly established based on the theory of energy conservation. Then, the transfer functions on the transient responses of wet cooling coils have been deduced using the method of Laplace transform. The transfer functions reveal the dynamic relation- ships between the inlet variables and the outlet ones of the cooling coils. Partial-fraction method and Newton–Raphson method are both used in the inversion of the transfer functions from the s-domain to s-domain. To make the dynamic model of wet cooling coils more adaptive, RBFNN method is employed to determine the coefficients of heat and mass transfer. Experiments have been done and manifested that the coefficients of heat and mass transfer by RBFNN will be of great value to the validity of the transient response model of wet cooling coils in this study.

Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. They have developed many forecasting methods, such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), grey model (GM) and artificial neural network (ANN), in the field of air-conditioning load prediction. However, none of them has enough accuracy to satisfy the practical demand. On the basis of these models existed, a novel forecasting method, called ‘RBF neural network (RBFNN) with combined residual error correction’, is developed in this paper. The new model adopts the advanced algorithm of neural network based on radial basis functions for the air-conditioning load forecasting, and uses the combined forecasting model, which is the combination of MLR, ARIMA and GM, to estimate the residual errors and correct the ultimate foresting results. A study case indicates that RBFNN with combined residual error correction has a much better forecasting accuracy than RBFNN itself and RBFNN with single-model correction.

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.

Experiments are conducted on human subjects to study the overall and the local thermal sensation and thermal comfort as well as the skin temperature distribution of the body in a recumbent posture under different environmental temperatures (21, 24, 26, and 29℃). Local skin temperatures are measured and 14 methods for the mean skin temperature overall are investigated. Statistical analysis showed that the Burton (3 points) method wiu obtain similar results for mean skin temperature compared with the other methods, e.g., Colin/Houdas (10 points), Hardy/DuBois (12 points), Stolwijk/Hardy (unweighted 10 points), and Mitchell/ Wyndham (unweighted 15 points), and is employed in this study due to its simplicity and convenience. Meanwhile, the relationship between skin temperature and thermal comfort and sensation is investigated and overall/local thermal sensation models based on the skin temperature are established according to the experimental data in this study.