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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]

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]

[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]