This study mainly explored the thermal comfort from the perspective of physiology. Three physiological parameters, including skin temperature (local and mean), electrocardiograph (ECG) and electroencephalogram (EEG), were investigated to see how they responded to the ambient temperature and how they were related to the thermal comfort sensation. A total of four ambient temperatures (21℃, 24℃ , 26℃ and 29℃ ) were created, while the other thermal conditions including the air velocity (about 0.05±0.01 m/s) and the air humidity (about 60±5 m/s) were kept as stable as possible throughout the experiments. Twenty healthy students were tested with questionnaire investigation under those thermal environments. The statistical analysis shows that the skin temperature (local and mean), the ratio of LFnorm to HFnorm of ECG and the global relative power of the different EEG frequency bands will be sensitive to the ambient temperatures and the thermal sensations of the subjects. It is suggested that the three physiological parameters should be considered all together in the future study of thermal comfort.

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.

Energy-cost allocation systems apportion heating and/or cooling costs among the individual apartments in centrally-metered buildings based on various measures of relative energy use or thermal comfort. The most commonly used systems measure one or more parameters related to the thermal output of the terminal elements. In this paper, a new method for energy-cost allocation purposes was developed and studied. Taking advantage of the theory of frequency response, the individual apartments’ energy consumption for cooling in summer may be calculated using several parameters that include indoor and outdoor temperatures and the values of solar radiant-intensity. Experiments were done in this study, which indicates that the method of frequency response is fairly good in calculating the energy use of cooling.

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.

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.