Development of a novel designs and assessment and selection system for green office buildings in China

China is facing severe problems in fossil fuel consumption and pollutant emission largely owing to the construction of buildings. Office buildings, as a major building type in China, contribute around 22% of the national fossil fuel energy use and 14% of carbon emissions. The identification of the most appropriate solution to energy saving and pollutant emissions reducing at the earlier design stage of office buildings is significantly important to China’s sustainability development and environmental protection.This PhD research aims to establish a simple and straight-forward assessment method that can predict fossil fuel energy use and the associated pollutant emissions of the Chinese office buildings at their early conceptual design stage (when the detailed material and constructional information is unavailable), and further, develop a computer- aided assessment and selection process that can identify the best design solution to the office buildings of China. This work is carried out through a standard research process including literature review, methodology development, computer model establishment, case study and results analysis with comparisons, followed by recommendations. As a result, the research provides a variety of important outputs, i.e., the life-cycle energy and air-pollutants estimation method, the generalized environmental impact metric system, and the green office building design solution assessment and selection system (GBAS). It has been demonstrated that the simplified energy and pollutants estimation method can predict the energy consumption and associated pollutant emissions at each office building life-cycle phase, based on the refined mathematical correlations and associated computerized toolkits. By using the generalized environmental impact metric system, the pollutant equivalent (PE), which reflects the combined environmental impact of the emission of four common pollutants, is derived and its values are discussed in detail. Based on the estimation of life-cycle energy and PE, the GBAS system is developed to identify the “best” design solution on both the quantitative survey and qualitative analyses.A combination of all the above outcomes leads to the development of a comprehensive computerized tool that can conduct faster assessment, optimization and selection of the “best” design solutions for Chinese office buildings at their very earlier stage of design. The prediction results have been proven to be rational, realistic and applicable to practical engineering projects.The outcomes of the research can help in the design of energy efficient and “green” office buildings in China, thus contributing to China’s sustainable development and environmental protection

Operational performance of a novel fast-responsive heat storage/exchanging unit (HSEU) for solar heating systems

In order for a solar heating system to provide heat immediately after sunrise, a fast response is needed to the heat demand of a serviced space. The majority of existing solar heating systems have a slow response time due to the large volume of water stored in the heat storage/exchanger unit (HSEU). This leads to a slow heat delivery cycle, which results in discomfort for the occupants and thus creates a huge barrier to the wide deployment of solar heating systems. To overcome this critical issue, a novel interactive heat storage/exchanging unit (HSEU) employing a double-tank configuration was developed. Unlike conventional HSEUs, which have a single tank acting as the heat storage and exchanging unit, the new HSEU is comprised of a small tank for heat exchange combined with a large tank for heat storage. The small tank enables fast transfer of solar heat to the heating loop fluid without having to heat up the large volume of water in the entire HSEU tank, whilst the large tank is used to store and exchange heat between itself and the small tank using a temperature-oriented control mechanism. To test the proposed design, the heat transfer between the first (solar loop) and second (heating loop) fluids, the heat and mass transfer between the small and large tank and the associated operational strategy were investigated experimentally and theoretically for comparison. A conventional single tank HSEU requires around 120 min to deliver heat to a served space, whilst the new interactive double-tank HSEU can provide heat to the served space in around 20 min, thus creating a heating system which can respond significantly faster than traditional systems. The investigation of the heat exchange effect between the solar and heating loop fluids showed that the new HSEU achieved a convective heat transfer coefficient of as high as 391 W/m2·K, which is 551% higher than that of a conventional tank. As a result, the solar thermal efficiency of the solar panel-array in the new HSEU based system was increased by 7.5% compared to conventional HSEU based systems

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability

Optimal Decision-Making Model of Agricultural Product Information Based on Three-Way Decision Theory

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability

Analytical and experimental study of an innovative multiple-throughout-flowing micro-channel-panels-array for a solar-powered rural house space heating system

This paper presents a combined analytical and experimental study of an innovative multiple-throughout-flowing micro-channel-panels-array applicable to a solar-powered rural house space heating system. This array, compared to the traditional one-to-one-connection panels-array, can significantly reduce the temperature difference between the head and real panels and thus increase the overall solar thermal efficiency and energy efficiency ratio (EER). The research methodology covers the theoretical analysis, experimental testing, model validation and system optimization. It is found that the analytical model has a good accuracy in predicting the performance of the multiple-throughout-flow micro-channels-panels-array, giving a discrepancy of less than 10%. In terms of the configuration and sizes of the array, 10 pieces of panels with 5 flow turns is regarded as the most favorite option. During the operation, decreasing flow rate of the fluid led to the increased EER of the panels-array. By converting the one-to-one-connection mode into the multiple-throughout-flowing mode, the overall solar thermal efficiency of the panels-array increases by around 10% and its energy efficiency factor (EER) decreases by 80% respectively. The research has addressed a novel solar-panels-array that can be well applied to solar thermal systems, thus making a significant contribution to the saving of fossil fuel energy consumption and reduction of carbon emission on global scale