Building Simulation最新文献

Using the behind-meter data, this study applied a comparison and optimization-based framework to evaluate the energy flexibility and resilience of distributed energy resources within existing houses during cold wave event. Comparative analysis demonstrates the effectiveness of high envelope insulation level in improving energy resilience, identifies impacts of distributed energy resources on variations of household electricity demand. Specifically, a 14.6% reduction in the median value of the normalized load of building group with low U-values, implementations of cogeneration system effectively suppressed variations of electricity load. Dynamic energy performances of on-site generators are evaluated based on high resolution data, energy flexibility of domestic hot water and thermostatically controlled loads were investigated through built demand response model. Results reveal that electrifying hot water demand offers additional power flexibility, the integration of fuel cell cogeneration system has proven to be an efficient energy resource, enabling on-site generation of both electricity and hot water, substantially reducing grid import. The extreme cold event resulted in significant spikes in space heating power consumption. The optimization results demonstrate that reducing the indoor setpoint temperature effectively decreases daily power consumption by approximately 5.0% per degree Celsius. These findings help acquire better understanding of interconnections between energy efficiency and resilience of residential energy-efficient measures.

Accurately predicting the chiller coefficient of performance (COP) is essential for improving the energy efficiency of heating, ventilation, and air conditioning (HVAC) systems, significantly contributing to energy conservation in buildings. Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently, which impedes accurate predictions. To overcome these challenges, this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network (GCN) enhanced by association rules. The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data. A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data. This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN. The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system. Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.

Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption. This study proposed a convolutional neural network (CNN) based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers. The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model. The dataset concerned three typical faults, including refrigerant leakage, evaporator fan breakdown, and condenser fouling. Then, the CNN model was trained to construct a map between the input and system operating conditions. Further, the performance of the CNN model was validated by comparing it with the support vector machine and the neural network. Finally, the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes. The results demonstrated in the pump-driven heat pipe mode, the accuracy of the CNN model was 99.14%, increasing by around 8.5% compared with the other two methods. In the vapor compression mode, the accuracy of the CNN model achieved 99.9% and declined the miss rate of refrigerant leakage by at least 61% comparatively. The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters, such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode, were essential features in system fault detection and diagnosis.

Connecting occupants to the outdoor environment and incorporating biophilic design principles are challenging in extreme Arctic climatic conditions. Existing Arctic housing models do not provide efficient thermal and daylight transitions which are essential for the well-being and cultural needs of their occupants. To address these challenges, this research develops free-running biophilic intermediate spaces, integrated into an existing Arctic housing model. Numerical simulation methods are employed to optimize the primary and secondary architectural design variables for 26 case studies of intermediate spaces. Primary variables include volume, transparency ratio, and orientation. Secondary variables include materials and physical adjacency. Temperature, Daylight Factor/Autonomy, and Energy Use are evaluated as performance indicators. Results reveal that free-running intermediate spaces with 6 meters depth and a transparency ratio above 50% provide efficient indoor–outdoor transitions regarding thermal, visual, and energy performance. Such architectural configurations contribute to an approximately 5% reduction in energy consumption within the housing unit compared to the baseline. Opening side windows prevents the risk of overheating during the summer by reducing the average indoor temperature of intermediate spaces by 7 °C but increases the overall energy consumption. As a potential alternative to double-glazing, polycarbonate sheets enable efficient thermal performance by increasing the average indoor temperature of intermediate spaces by approximately 15 °C during the cold Arctic seasons. Using polycarbonate sheets results in a 16.6% reduction in energy consumption compared to using double-glazing material in intermediate space, and a 26% reduction from the baseline. Research outcomes contribute to efficient indoor–outdoor connections and energy efficiency in Arctic housing.

In the context of escalating global energy demands, urban areas, specifically the building sector, contribute to the largest energy consumption, with urban overheating exacerbating this issue. Utilizing urban modelling for heat-mitigation and reduction of energy demand is crucial steps towards a sustainable built-environment, complementing onsite energy generation in the design and development of Net-zero Energy (NZE) Settlement, especially in the context of Australian weather conditions. Addressing a significant gap in existing literature, this study offers empirical analysis on the climate and energy efficacy of integrated heat mitigation strategies applied in 14 neighbourhood typologies located in Sydney, Australia. Examining the application of cool materials on roads, pavements, and rooftops, alongside urban vegetation enhancement, the analysis demonstrates scenario effectiveness on heat mitigation that leads to reduce ambient temperature and energy demands along with CO₂ emissions within the neighbourhoods. Considering building arrangement, built-area ratio, building height, and locations, ENVI-met and CitySim are utilized to assess the heat-mitigation and the energy demand of neighbourhoods, respectively. Results indicate that mitigation measures can lead up to a 2.71 °C reduction in ambient temperature and over 25% reduction in Cooling Degree Hours, with a 34.34% reduction in cooling energy demand and overall energy savings of up to 12.49%. In addition, the annual energy-saving yields a CO₂ reduction of approximately 141.12 tonnes, where additional vegetation further amplifies these reductions by enhancing CO₂ absorption. This study showcases the pathway towards achieving NZE goals in climates similar to that of Australia, highlighting significant benefits in heat-mitigation, environmental impact, and energy-savings.

The dynamic characteristics of different airflows on micro-scales have been explored from many perspectives since the late 1970s. On the one hand, most analytical tools and research subjects in previous contributions vary significantly: some only focus on fluctuant velocity features, while others pay attention to directional features. On the other hand, despite the wide variety of existing analytical methods, they are not systematically classified and organized. This paper aims to establish a system including state-of-the-art tools for airflow analysis and to further design a holistic toolkit named Airflow Analytical Toolkit (AAT). The AAT contains two tools, responsible for analyzing the velocity and direction characteristics of airflows, each of which is integrated with multiple analytical modules. To assess the performance of the developed toolkit, we further take typical natural and mechanical winds as cases to show its excellent analytical capability. With the help of this toolkit, the great differences in velocity and directional characteristics among different airflows are identified. The comparative results reveal that not only is the velocity of natural wind more fluctuating than that of mechanical wind, but its incoming flow direction is also more varying. The AAT, serving as a powerful and user-friendly instrument, will hopefully offer great convenience in data analysis and guidance for a deeper understanding of the dynamic characteristics of airflows, and further remedy the gap in airflow analytical tools.

Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types. As a solution to this issue, this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling, and in particular code compliance modeling for commercial buildings. The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load schedules is introduced. After that, the database for these building-space-specific loads and load schedules is presented. In addition, one case is studied to demonstrate the applications of these loads and load schedules. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). The energy results simulated by using these three methods are compared, which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.

To investigate the impact of building heat transfer on roof snow loads, roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature, humidity, wind speed, and precipitation, and a multi-layer snowmelt model considering the building heat transfer. Firstly, the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests. The relationship between meteorological conditions, snow cover characteristics, and thermal coefficients of snow loads in three representative sites is then studied. Furthermore, the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010, and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately. Finally, the equations in this paper are compared with the thermal coefficients in the main snow load codes. The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads. In the cold zone where the snow cover lasts for a long time and does not melt easily, the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings. Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients. When the indoor temperature is too low or the roof insulation is good, the roof snow load may exceed the ground snow load. The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper, and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.

Retrofitting a historic building under different national goals involves multiple objectives, constraints, and numerous potential measures and packages, therefore it is time-consuming and challenging during the early design stage. This study introduces a systematic retrofitting approach that incorporates standard measures for the building envelope (walls, windows, roof), as well as the heating, cooling, and lighting systems. Three retrofit objectives are delineated based on prevailing Chinese standards. The retrofit measures function as genes to optimize energy-savings, carbon emissions, and net present value (NPV) by employing a log-additive decomposition approach through energy simulation techniques and NSGA-II, yielding 185, 163, and 8 solutions. Subsequently, a weighted sum method is proposed to derive optimal solutions across multiple scenarios. The framework is applied to a courtyard building in Nanjing, China, and the outcomes of the implementation are scrutinized to ascertain the optimal retrofit package under various scenarios. Through this retrofit, energy consumption can be diminished by up to 63.62%, resulting in an NPV growth of 151.84%, and maximum rate of 60.48% carbon reduction. These three result values not only indicate that the optimal values are achieved in these three aspects of energy saving, carbon reduction and economy, but also show the possibility of possible equilibrium in this multi-objective optimization problem. The framework proposed in this study effectively addresses the multi-objective optimization challenge in building renovation by employing a reliable optimization algorithm with a computationally efficient reduced-order model. It provides valuable insights and recommendations for optimizing energy retrofit strategies and meeting various performance objectives.

The global prevalence of infectious diseases has emerged as a significant challenge in recent years. Surface transmission is a potential transmission route of most gastrointestinal and respiratory infectious diseases, which is related to surface touch behaviors. Manual observation, the traditional method of surface touching data collection, is characterized by limited accuracy and high labor costs. In this work, we proposed a methodology based on machine learning technologies aimed at obtaining high-accuracy and low-labor-cost surface touch behavioral data by means of sensor-based contact data. The touch sensing device, primarily utilizing a film pressure sensor and Arduino board, is designed to automatically detect and collect surface contact data, encompassing pressure, duration and position. To make certain the surface touch behavior and to describe the behavioral data more accurately, six classification algorithms (e.g. Support Vector Machine and Random Forest) have been trained and tested on an experimentally available dataset containing more than 500 surface contacts. The classification results reported the accuracy of above 85% for all the six classifiers and indicated that Random Forest performed best in identifying surface touch behaviors, with 91.8% accuracy, 91.9% precision and 0.98 AUC. The study conclusively demonstrated the feasibility of identifying surface touch behaviors through film pressure sensor-based data, offering robust support for the calculation of viral load and exposure risk associated with surface transmission.