Xi Chen

Bio: Xi Chen is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Passive solar building design & Photovoltaic system. The author has an hindex of 18, co-authored 31 publications receiving 919 citations. Previous affiliations of Xi Chen include Open University of Hong Kong.

A comprehensive review on passive design approaches in green building rating tools

Abstract: Buildings are the major consumers of energy in Hong Kong and most urban areas in the world. Building environmental assessment schemes, and green building rating tools (GBRTs) have been adopted by architects, engineers and researchers for more than 20 years to help promote more sustainable construction activities. Each rating tool highlights energy use as a significant portion of the assessment and provides guidance on more energy efficient strategies. Building energy efficiency can usually be improved by both passive and active technologies. Active design involves making more energy efficient heating, ventilation, air-conditioning (HVAC) systems, hot water production, lighting and any other building services application, whereas passive design focuses more on building envelope related aspects determined by the architectural design so as to reduce the demand of the building for energy. Recently, there has been renewed interest in passive strategies because of the low extra investment and the potential benefits in energy saving. The passive design approach has also been recognized in the latest versions of green building rating tools. Five representative rating systems, which all developed their own passive design criteria leading to the award of credits, are subject to comparative examinations in respect of the comprehensiveness, effectiveness and accuracy of each criterion in this paper. Passive design criteria including the building layout, envelope thermophysics, building geometry, air-tightness and infiltration performance and their effects on building energy consumption are also comprehensively reviewed. The results show that a holistic design approach based on passive energy saving strategies proves to be an effective way to reduce building energy budgets. However, more consolidated weighting systems to enable comparison of different passive strategies should be incorporated in green building rating tools based on further sensitivity and parametric studies.

A review of uncertainty analysis in building energy assessment

Abstract: Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.

A review on optimization methods applied in energy-efficient building geometry and envelope design

Abstract: Building envelope parameters and geometric configurations can considerably influence the building energy performance. However, determining the best trade-offs of different building shape and envelope configurations to yield near-optimal design alternatives with respect to their energy performance is not a straight-forward task. Consequently, different methods have been utilized to optimize building envelope parameters and geometric configurations to achieve better energy performance. The objective of this paper is to provide an extensive review of the optimization methods and their application in energy-efficient architectural building design to better identify the potentials and applicability of different optimization methods. This paper reviews the optimization research, where building envelope parameters and geometric configurations are considered remarkably as the optimization independent variable(s) and building energy consumption/demand is included as an objective in the optimization process. The associated derivative-free and derivative-based optimization methods and their application in energy-efficient building design are included in this review. In addition, decision-making approaches are discussed for multi-objective optimizations. Current optimization tools are demonstrated. Finally, crucial considerations, including limitations and suggestions for the related future studies are concluded.