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In this paper, we estimate the energy efficiency of educational buildings with the case study of buildings in City University of Hong Kong by constructing an energy demand stochastic frontier model. The model is estimated by using the university statistical data from 2011 to 2015. For the consistent frequency of data among the variables, we have adopted the quadratic-match sum method to convert annual university report data into a monthly dataset. Our result shows the average energy efficiency is 0.87, implying that 13% of total energy consumption can be saved. We then calculate how much of the energy saving potential has been achieved by constructing the performance score, which increases from 0.08 to 0.17. It implies that the campus performs more efficiently in saving energy over time. We further develop econometric decomposition analysis based on the energy demand frontier model to identify the factors affecting energy consumption. It suggests that research activities account for a large share of overall energy consumption. Analysis on energy end-use shows university should improve efficiency in lab instrument as it is least efficient among the four usages. We expect this paper can provide the fundamental and methodological guideline for university-scale energy efficiency estimation. • Estimating energy efficiency of educational buildings needs to consider the composition of research related factors. • Energy efficiency of campus building presents increasing tendency with seasonal cycle over time. • Research related variables affect significantly on energy efficiency of educational buildings. • Energy use in lab instrument needs to be effectively managed to save energy. [ABSTRACT FROM AUTHOR]

Abstract: Prefabrication has been increasingly used in buildings. It is recognised as a solution to reduce waste arising during design and construction phases. However, there is little emphasis on life cycle design issues for prefabricated buildings located in dense high-rise building environments. The purpose of this paper is to review the application and identify benefits and impediments of design for deconstruction and Industrialised, Flexible and Demountable building systems when applied to precast concrete construction. The paper presents the results of a comprehensive literature review, and two case studies of recently completed institutional buildings using prefabrication. The literature review shows that, so far, design for deconstruction is not a common practice in the building industry. The case studies showed some limitations such as the dense urban environment conditions and limited site area. The promotion of a closed-loop material cycle is critical to contribute to sustainability thus minimising CO2 emissions, natural resources consumption. [Copyright &y& Elsevier]

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis. [ABSTRACT FROM AUTHOR]

Semi-transparent photovoltaic (STPV) windows, which can not only admit daylight into space but also generate electricity, have been widely used as a part of building components in recent years. This paper compares the overall energy performance between STPV windows and commonly used energy-efficient windows, viz. double-pane and Low-E windows, in Hong Kong. A new type of see-through amorphous silicon (a-Si) photovoltaic (PV) module was adopted, and this PV module has higher efficiency compared with other commercialized a-Si PV modules with the same visible transmittance. First, a series of testing were carried out to get the technical parameters of this PV module. Second, the simulation models were developed theoretically, and power, thermal and daylighting performance of different windows were analyzed by EnergyPlus accordingly together with the input weather data of typical meteorological year (TMY) in Hong Kong. Then, based on the simulated energy uses of the air-conditioning and the artificial lighting of the room, as well as the power generation of the STPV windows themselves, the overall energy performance was evaluated. The results indicate that STPV glazing can save up to 18% and 16% total electricity per year respectively in comparison with the clear single and double-pane glazings in cooling dominated areas like Hong Kong. The overall energy performance of Low-E glazing and STPV glazing is very close to each other, which means that STPV glazing is quite energy-efficient. In Hong Kong, southwest is the best orientation for power generation for STPV windows and south is the optimum orientation for STPV window installation in terms of achieving the best overall energy performance. [ABSTRACT FROM AUTHOR]

• A POE-integrated simulation method is proposed to address energy occupant behavior. • Opening windows and operating AC were found to co-exist which affects simulation accuracy. • The new method improves energy simulation accuracy by 14% for domestic areas. • The method improves energy simulation accuracy by 16% for total building areas. Occupant behavior plays a critical role in building energy consumption, particularly in residential buildings. However, occupant behavior is complicated and varies significantly from case to case. Also, energy modeling of high-rise buildings is far less explored than that of low- or medium-rise buildings. This paper aims to improve accuracy in building energy simulation by utilizing Post Occupancy Evaluation (POE) data to calibrate energy model. Drawing on a review of the literature of occupant behavior and building energy modeling, the paper provides a calibration method of integrating POE data into an energy model, which is demonstrated using a real-life typical 40-storey residential building in Hong Kong. The developed method addresses seven updated input parameters, namely, schedule, devices, air-conditioners, windows, lights, domestic hot water, and cooking. By comparing the two energy modeling processes, i.e. with and without POE input, and their resultant estimated energy consumption, the paper quantifies the impact of occupant behavior on building energy consumption. Annual metered data together with energy bills obtained from the POE were utilized to validate the models. The results show that the use of the developed POE-integrated method helped to improve accuracy in energy consumption prediction by 14% for the total building floor area and by 16% for the total residential area. From examining the impacts of the seven input parameters, the paper reveals the key energy use sensitive occupant behaviors within high-rise residential buildings in Hong Kong. These include: the number of residents in each unit; adoption of window type and split type of air-conditioners; window and air condition operation modes; cooking time on weekdays and weekends; and time spent on hot water showering. The developed method can assist building designers and services engineers to estimate building energy use more accurately and provides a scenario analysis tool for clients and facility managers to develop effective energy conservation strategies. [ABSTRACT FROM AUTHOR]

Modeling buildings’ energy use is an effective strategy for identifying energy saving potential and seeking for energy efficient building solutions. However, there exist significant challenges for such modeling, particularly of high-rise buildings. The aim of this paper is to examine such challenges drawing on the case of high-density high-rise office buildings in the hot and humid climate of Hong Kong. The challenges are examined in relation to four aspects, namely, modeling approach, modelling tools, data availability and atmospheric conditions. A framework of strategies is developed for addressing the challenges. The strategies are then illustrated through a case study with a hypothesized high-rise office building in Hong Kong. Essential to achieving accuracy of modeling buildings’ energy use are found to be the collection and verification of building information and specification of thermal zones and schedules for modeling. The lack of usable data of energy use in high-rise buildings is considered to be a major barrier to verifying modeled results. To establish an integrated model with energy modelling and CFD software should effectively address the challenges. [ABSTRACT FROM AUTHOR]

Application of the shading-type Building-Integrated Photovoltaic (BIPV) claddings is one of the most effective approaches for reducing energy use in buildings and making buildings environment-friendly. PV modules are used as external shading devices and insulation panels as well. In this paper, the energy performance of the shading-type BIPV claddings is investigated under dynamic conditions. The design parameters of the shading-type BIPV claddings, including the tilt angles and azimuth angles of PV modules, the window to wall ratio of the building and the overhang length, are discussed based on the meteorological conditions of Hong Kong. Optimum designs of the shading-type BIPV claddings are reported for buildings with different orientations and window to wall ratios. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a simplified energy performance assessment method for existing buildings in cooling season, which is based on energy bill disaggregation and energy performance analysis. This method requests very limited building energy data and can effectively assess the energy performance at building and system levels, and disaggregate the whole-building consumption into consumptions of three groups of end-uses. This method is based on two basic energy balance principles, i.e., the electricity consumption balances at building level and the cooling energy balances between demand side and supply side of HVAC systems. An optimization algorithm is developed to establish best possible cooling energy balances by minimizing the balance residuals and therefore to disaggregate the energy consumption of different users. The performance of the proposed method was validated in two existing buildings in Hong Kong and Beijing. The results, including the disaggregated energy consumptions and the key energy performance indicators of HVAC system, agreed well with the “measured” data in mechanical cooling months. [Copyright &y& Elsevier]

Abstract: Proper design, reliable control and health of variable–air–volume (VAV) terminals are essential for reliable operation and energy efficiency of VAV air-conditioning systems. This paper presents an online fault diagnosis tool for pressure-independent VAV terminals and its implementation. The fault diagnosis tool is developed using a robust fault detection and diagnosis strategy for VAV terminals. Cumulative sum (CUSUM) control chart with estimated process parameters is utilized to detect faults in VAV terminals. A rule-based fault classifier is designed and utilized to find the sources of faults in VAV terminals. The fault diagnosis tool relies only upon the sensor data and control signals that are commonly available in building management control systems (BMCS). The fault diagnosis tool can be installed in BMCS to online read the operating data of all VAV terminals in a building through local area network and diagnose faults of VAV terminals in the building. The fault diagnosis tool has been validated in a large-scale office building located in Hong Kong and can effectively find all faults occurring in VAV terminals. [Copyright &y& Elsevier]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

The rapid growth of energy consumption in buildings has become an alarming threat to the climate. Various building-level energy saving measures have been studied and adopted to achieve energy efficiency. However, high-rise public residential buildings in subtropical climate as an important contributor to building-level energy consumption have received insufficient attention of research. The aim of this paper is to analyse the energy use of high-rise residential buildings in subtropical climate and examine the impacts of different energy saving measures for developing strategies for achieving very low-energy high-rise buildings. A typical high-rise (40-storey) public residential building in Hong Kong was selected as the case building, which has the energy use intensity of 106.6 kWh/m2 for the total building area and of 153.8 kWh/m2 for the area inside the residential units. The energy model of the case building developed using EnergyPlus software was carefully calibrated using metered energy use data and expert consultations adopting a systematic approach. Sensitivity analyses were then conducted using the parametric analysis tool jEPlus to quantify the impacts of different energy saving measures on energy use intensity reduction. It was found the measures in relation to human behaviours have the largest impact, followed by building service system efficiency and renewable energy. Building form and building envelope have the modest impact. The findings enhance the knowledge of subtropical high-rise public residential buildings' energy performance, and the developed and calibrated energy model could be used to explore more strategic energy saving measures in the long term. The quantified impacts of the measures should inform design decisions of low-energy housing production in high-rise sectors. • A 40-storey building is examined with energy use 153.8 kWh/m2 for residential area. • Energy model uses EnergyPlus software with sensitivity analyses tool jEPlus. • Behaviours have largest impact on energy use, then building services and renewables. • Building form and envelope have the modest impact on energy use reduction. • Systemic strategies are suggested for very low-energy high-rise residential buildings. [ABSTRACT FROM AUTHOR]