Modeling of window behavior is a key component for building performance simulation, due to the significant impact of opening/closing windows on indoor environment and energy consumption. The predictions of existing models cannot well reflect actual window behavior, the prediction accuracy still needs to be improved. The Gauss distribution model is a new machine-learning technique which has achieved successful applications in many fields because of its special advantages (i.e. simple structure, strong operability and flexible nonparametric inference ability) compared to existing models. This paper presents results from a study using the Gauss distribution model to predict window behavior in office building. The data used in this study were from a real building located in Beijing, China, and covered two transitional seasons (from October 1 to November 15, 2014 and from March 15 to May 16, 2015), when natural ventilation was fully applied. When modeling, three types of input variables, i.e., indoor temperature, outdoor temperature and their combination were used. This work validates the importance of selecting suitable input variables when developing Gauss distribution model. This study also compared the prediction performance between the Gauss distribution modeling approach and the Logistic regression modeling approach, which is the most popular method used to model occupant window behavior in buildings. The results showed that Gauss distribution models could provide higher prediction accuracy, with 9.5% higher than Logistic regression model when using suitable inputs. This paper provided a novel modeling method that can be used to predict window states more accurately in office buildings.
The air source heat pump (ASHP) is developing rapidly and is widely used for space heating due to its potential for increasing energy efficiency and reducing greenhouse gas emissions. The choice of appropriate low global warming potential (GWP) alternative refrigerants is one of the challenges that ASHP systems face. Alternative refrigerants also affect the energy performance of these systems. Thus, it is essential to evaluate the performance of ASHP using environmentally friendly refrigerants to facilitate reasonable refrigerant selection. A theoretical model for simulating ASHP performance with different refrigerants is developed in this study. Experiments are conducted to validate the theoretical model. The simulation and the experimental results are found to be in good agreement. The ASHP performance indices, such as compression ratio (CR), discharging temperature (DT) and coefficients of performance (COP), are investigated using R22, R417A, R410A, R134a, R152a, R161 and R1234yf as working fluids. The results show that R152a has an average COP of 2.7% higher than R22, and R161 has an average COP of 1.4% higher than R22. R152a and R161 also have a higher CR but a lower DT than R22 under the same design conditions. In addition, R152a and R161 have ozone depletion potentials (ODP) of zero and extremely low GWPs; thus, they can be candidates to replace R22 in ASHP heating systems. This research provides a reference on refrigerant replacements for ASHP heating systems in North China.
This study presents a theoretical study on the super thin and conductive thermal absorber with built-in corrugated channels on the basis of previous field experiments. The flow and heat transfer characteristics of the corrugated channels are simulated to identify the factors affecting photovoltaic/thermal (PV/T) system efficiency. The influences of the structural parameters such as the corrugation number, the corrugation area, and the flow channel width on the water outlet temperature and heat collection are discussed in order to support the structural optimization design of the hybrid PV/T system. The simulation results were validated to be in good agreement with experimental results. The results indicate that increasing inlet water velocity leads to a decrease in the outlet temperature. It was found that the corrugation area and the flow channel width have impacts on the outlet temperature of the hybrid PV/T collector panel. When the flow channel width of the absorber plate is reduced from 4 mm to 3 mm, the outlet temperature attained is between 298 and 302 K, and the heat collection is in the range of 16.2–51.4 MJ/h. This led to an increase in the amount of heat collected by 18.6%.
In this article, the daily brightness temperature data from January 2006 to May 2020 of China’s geostationary meteorological satellite FY-2E/G were used to identify the brightness temperature differences before deep and shallow earthquakes in the study area using wavelet transform and the relative wavelet power spectrum (RWPS) methods. The objective was to explore the characteristics of thermal infrared (TIR) radiation anomaly changes before deep and shallow earthquakes in Northeast China by carrying out anomaly extraction and data analysis. The research has shown that five significant earthquakes experienced TIR radiation anomalies in the vicinity of the epicenter approximately 1–2 months before the event. The amplitude of the anomaly ranged from seven to twenty times higher than average, and the anomaly lasted about 3 months. The infrared radiation anomaly characteristics before the earthquake were especially significant in the case of two earthquakes in the Songyuan area. From the research, it was concluded that the TIR radiation anomaly could act as a short-term precursor for earthquake prediction. The method employed in this study would provide great support for predicting deep and shallow earthquakes in Northeast China using satellite thermal infrared technology.
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