Taking a certain housing in Guangzhou as an example, we conduct the field measurement of the microclimate at fixed points for air temperature, relative humidity, black globe temperature and wind speed, etc. We investigate the effects of manmade lake, shade of trees and ground surface character on outdoor thermal environment, and make a quantitative analysis on the weighting position of the landscape design factors in design behavior. The study intends to explore a method to improve the thermal environment of residential quarters by changing the corresponding factors.
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This paper studies the summer natural wind environments inside an airport terminal building under two cases by the method of computational fluid dynamics (CFD). The two cases have different opening areas for glass curtain wall. Case 1 has the opening ratio of 10% while Case 2 has 30%. The paper also uses DeST to simulate the annual natural temperature distribution under two kinds of opening ratio in a whole year. At last, the energy consumptions under two conditions-without opening for glass curtain wall all through the year and with the opening ratio of 30% when air conditioning is not run are calculated respectively. The numerical simulation results show that: when the opening ratio of the glass curtain wall is 10%, the summer indoor natural wind environment is bad. The air change rate is less than 6times/h. But when the opening ratio is increased to 30%, the summer indoor natural wind environment becomes better. The air change rate goes above 10times/h. In addition, if 10% opening area of the glass curtain wall is opened completely all through the year, there are at least 2707 hours in which air conditioning is not needed for the airport terminal building. But when 30% opening area is opened completely in a whole year, there are at least 5398 hours in which air conditioning is not needed. In a whole year, if air conditioning is run to make heat or cool when the indoor natural temperature is less than 16°C or higher than 29°C, opening 30% of the glass curtain wall at the time that air conditioning is not run can save 21% of the energy that the case without opening in a whole year consumes. Increasing natural ventilation in summer can decrease the cooling load effectively. But in winter, increasing natural ventilation may increase the heating load.
Intelligent diagnosis is an important means of ensuring the safe and stable operation of chillers driven by big data. To address the problems of input feature redundancy in intelligent diagnosis and reliance on human intervention in the selection of model parameters, a chiller fault diagnosis method was developed in this study based on automatic machine learning. Firstly, the improved max-relevance and min-redundancy algorithm was used to extract important feature information effectively and automatically from the training data. Then, the long short-term memory (LSTM) model was used to mine the temporal correlation between data, and the genetic algorithm was employed to train and optimize the model to obtain the optimal neural network architecture and hyperparameter configuration. Finally, a transient co-simulation platform for building chillers based on MATLAB as well as the Engineering Equation Solver was built, and the effectiveness of the proposed method was verified using a dynamic simulation dataset. The experimental results showed that, compared with traditional machine learning methods such as the recurrent neural network, back propagation neural network, and support vector machine methods, the proposed automatic machine learning algorithm based on LSTM provides significant performance improvement in cases of low fault severity and complex faults, verifying the effectiveness and superiority of this method.
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