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This paper provides a critical review on the building neighborhood properties influencing the energy and airflows in urban neighborhoods. Specifically, the review focus is on the multi-scale modeling required to quantify this influence of the building neighborhood properties on the energy consumption in buildings. The energy consumption patterns of buildings located in dense city centers are highly dependent on the surrounding urban neighborhood, compared to the low density, suburban/rural regions, where the building energy consumption patterns are similar to an isolated building energy consumption patterns. Due to the complex nature of the outdoor airflow around the buildings in urban neighborhoods, a practical modeling approach utilizes multi-scale modeling to account for different spatial and temporal scales for the relevant transport processes. Specifically, this modeling approach aims to identify the most important neighborhood properties influencing building energy consumption. The urban morphology parameters, such as the urban plan area density, frontal area density, and mean height of the buildings represent successful examples of emerging properties suitable for development of generalized solutions and physical models at the neighborhood scale. This paper also reviews different modeling approaches that account for the impacts of the urban neighborhood properties on the thermo-fluid property of the air, surfaces, and sky in the built environment as the required inputs for accurate assessment of building energy consumption. Furthermore, these emerging properties of urban neighborhoods directly affect (1) the mitigation strategies for a better adaptation, (2) design performance metrics of neighborhoods for the green building rating systems, and (3) socio-environmental factors. [ABSTRACT FROM AUTHOR]

In a changing outdoor climate, new buildings as well as the existing building stock need to adapt in order to keep providing their inhabitants and users a comfortable and healthy indoor environment, with a minimum or - preferably - no increase in energy consumption. In this paper, the effectiveness of six passive climate change adaptation measures applied at the level of building components is assessed using building energy simulations for three generic residential buildings as commonly built in - among others - the Netherlands: (1) detached house; (2) terraced house; (3) apartment. The study involves both residential buildings that are built according to the regulations and common practice in 2012, and residential buildings that were constructed in the 1970s, with a lower thermal resistance of the opaque and transparent parts of the building envelope. The climate change adaptation measures investigated are: (i) increased thermal resistance; (ii) changed thermal capacity; (iii) increased short-wave reflectivity (albedo); (iv) vegetation roofs; (v) solar shading; and (vi) additional natural ventilation. This paper quantifies the effectiveness of these climate change adaptation measures for new residential buildings as well as for renovation of the current building stock. The performance indicator is the number of overheating hours during a year. It is shown that exterior solar shading and additional natural ventilation are most effective for this performance indicator. Furthermore, increasing thermal insulation to reduce energy use for heating demands additional measures to prevent overheating. [ABSTRACT FROM AUTHOR]

Abstract: Building energy computer simulation software is a useful tool for achieving sophisticated design and evaluation of the thermal performance of buildings. For successful thermal and energy simulation of buildings, it requires hourly weather data such as dry bulb air temperature, relative humidity, solar radiation, wind speed, etc. Nowadays, an urban city faces a problem of an urban heat island which causes the urban area to have a higher air temperature than the rural region. Since the currently available weather dataset used in building simulation software mainly comes from weather stations located in remote and rural areas, the impact of the urban heat island on thermal and energy performance of buildings may not be effectively reflected. This paper reports an approach to construct a modified typical meteorological weather file, taking into account the urban heat island effect in the summer season. Field measurements have been carried out in the summer months and the corresponding urban heat island intensities were then determined. With a morphing algorithm, an existing typical meteorological year weather file was modified. An office building and a typical residential flat were modeled with a renowned building energy simulation program EnergyPlus. Computer simulations were conducted using the existing and modified typical meteorological year weather files. It was found that there was around a 10% increase in air-conditioning demand caused by the urban heat island effect in both cases. The implications of this and further work will also be discussed in this paper. [Copyright &y& Elsevier]

From an economic point of view, it is crucial to minimize the life cycle costs (LCC) of buildings undergoing energy renovations, hence an optimization approach is needed. Building energy use and power demand as well as energy efficiency measures are important issues while performing an LCC optimization. Thus it is of great importance to accurately predict the building energy use and power demand before and after energy renovation. This paper aims to address the performance of an in-house LCC optimization software, OPERA-MILP, which has a rather fast optimization procedure. The aim is fulfilled through comparison with building energy simulation software IDA ICE before and after cost-optimal energy renovation. Three historic buildings with different layout and construction properties are used as a case study. The results show good agreement in the calculations of buildings' power demand and energy use between OPERA-MILP and IDA ICE. The percentage difference in calculated annual energy use and buildings' power demand with OPERA-MILP compared to IDA ICE is shown to be maximum 11% and 8% for the studied climate zones, respectively. Total impact on LCC is estimated to be equal to or less than 8%. [ABSTRACT FROM AUTHOR]

Bioclimatic potential analysis is one of the starting points for bioclimatic building design. However, as climate changes are being brought into the spotlight, bioclimatic potential is being put into question as well, because traditionally used passive strategies at a specific location may no longer represent properly balanced approach. Therefore, the purpose of this paper was to systematically evaluate bioclimatic potential of the selected five locations. At these locations, bioclimatic potential was observed separately for each of the last five decades. In the second part, present and future energy performance of one bioclimatic and one non-bioclimatic real residential building was simulated. The results show that yearly balance between heating and cooling passive strategies changed through time in all the locations. For example, the use of overheating prevention strategies is becoming more significant than it used to be in the past. Specifically, the period of year when shading is needed to achieve thermal comfort increased by 2–7% points, depending on location. Energy performance analysis of the selected buildings showed that by 2050 both analysed buildings will become cooling dominated and that by 2050 the current design solutions in bioclimatic buildings will become irrelevant or at least extremely inefficient. In general, in temperate climate zone the prevailing bioclimatic strategies integrated in architecture focus on heating season. Therefore, bioclimatic strategies in a particular location must be re-evaluated in order to design new and retrofit existing energy efficient contemporary buildings with comfortable indoor thermal conditions. [ABSTRACT FROM AUTHOR]

The approaching wind velocity is an important input parameter in wind-driven ventilation studies. The approaching wind velocity at a certain height must be approximated from the measured meteorological wind velocity because the wind data in weather data files are usually measured at a meteorological station at only one given height. The most commonly used approximation method is the power law, in which the power law index (PLI) is only one parameter. Its typical values are provided in some data sources, the use of which generally involves several assumptions. An important assumption is that it is constant. We conducted observations of the wind velocity profiles above a high-density area in Tokyo, Japan, using a Doppler LIDAR system. Our observations revealed that the PLI value is not a constant but rather a time-dependent variable. In ventilation studies, neglecting this phenomenon possibly limits the usefulness of the results because the ventilation airflows depend on the PLI value in given building characteristics and given weather data. This paper presents a probabilistic simulation model for evaluating the performance of natural ventilation, which takes the uncertainties in the PLI values into account. The simulation results provide a set of possible values for savings in cooling energy that are possible with natural ventilation. Different choices of PLI values can lead to errors of up to 45% in the estimation of potential savings in cooling energy when using natural ventilation. This study can support building designers and engineers in the reasonable design of naturally ventilated buildings. [ABSTRACT FROM AUTHOR]

The European Directive 844/2018, the EN 16798–1 and −2, and the ISO 17772 Standards establish that when designing a new building or energy retrofitting an existing building, the Indoor Environmental Quality must be considered. One aspect of IEQ is thermal comfort, which is affected by the mean radiant temperature, t r. This variable is the only one that an HVAC system cannot control directly. In fact, it depends on the temperatures of the surfaces surrounding the subject which are influenced by the air temperature. It can be calculated using the methods envisaged by the ISO EN 7726 standard or alternative approximate methods. It is therefore important to understand whether the differentt r calculation methods affect the PMV value and, therefore, the classification of the thermal environment according to the EN 16798-1 Standard. For many years, building dynamic energy simulation software tools which integrate energy analyses with the evaluation of thermal comfort have been used for the design and verification of the energy performance of buildings. This paper compares different methods of calculating the mean radiant temperature used by Energy Plus with the Design-Builder interface and analyses the influence of the results on the evaluation of thermal comfort in three buildings. • The evaluation of t r by building simulation software can exhibit high errors. • Windows, climate, and subject position affect the reliability of simplified methods. • Zone Averaged method is effective only at the centre of the room. • Surface Weighted method exhibits lower errors near windows. • Simplified methods exhibit high uncertainties in thermal comfort assessment. [ABSTRACT FROM AUTHOR]

It is of great energy-saving significance to provide accurate occupancy schedules for building energy simulation. Inaccurate occupancy information can lead to biased results. However, the reference occupancy schedules provided by the standard documents are fixed and differ significantly from the actual occupancy fluctuations. Moreover, the large amount of accurate occupancy information is difficult and expensive to obtain, limiting the development of occupancy fluctuation pattern exploration and occupancy prediction. In this paper, a framework is proposed to provide accurate and flexible occupancy schedules based on occupancy-related variables and occupancy information extracted from surveillance videos through Convolutional Neural Network-based density estimation methods. Machine learning algorithms are used in the framework to predict occupancy owing to their powerful predictive capabilities. A case study based on educational buildings is conducted to examine the performance of the framework and to compare its predicted occupancy schedules with those provided by national standard documents. The average recognition accuracy of the proposed framework for different crowd densities is as high as 95.67%, and the mean absolute percentage error for the prediction of the occupancy by the proposed framework is only 16.88%. The average deviation degree between the predicted occupancy rate schedules and the actual occupancy rate schedules is reduced from 676.08% to 23.19%. The average deviation degree of building energy simulation results caused by the occupancy rate schedule is reduced from 42.56% to 0.93%. These results provide conclusive evidence that the proposed framework can provide realistic and flexible occupancy schedules to improve the accuracy of building energy simulation. • A framework for crowd counting and prediction in public buildings with high-density scenes. • Occupancy information obtained from images by CNN-based density estimation methods. • Average recognition accuracy of 95.67% for different crowd densities. • Average all-day prediction accuracy of 83.12% for occupancy of the target building. [ABSTRACT FROM AUTHOR]

Building energy simulation (BES) tools are fundamental for predicting energy performance and comfort. However, detailed models are computationally complex and demand high simulation times. These lead to difficulties in parametric runs and numerical optimizations. Performing numerous retrofit scenarios is hardly feasible, especially in multi-zone buildings with complex geometries. This paper introduces a novel approach to model order reduction (MOR) of BES models. The approach utilizes a deep learning-based unsupervised convolutional neural network autoencoder (CNN-AE). The method decomposes complex time series data derived from detailed simulations into lower dimension features. The low-dimension representations can be grouped through clustering algorithms to build a reduced-order model (ROM). The approach in this study automatically finds archetype zones of the original model that represent the energy behavior of a group of rooms, and removes redundant ones. The energy demand of the whole building can be estimated through these archetype zones. Our investigation shows that CNN-AE can be efficiently applied to reduce complex building energy simulation models. As proof of concept, a detailed model of a multi-zone campus building with 889 thermal zones is compared to the ROM derived from the CNN-AE. Comprehensive autoencoder hyperparameter training to optimize the accuracy of the model is provided. The ROM supports different purposes, such as energy scenario developments, with a total error of less than 1% compared to the original model, and reduced simulation times by a factor of more than 16. • A novel approach to MOR for BEM through convolutional neural network autoencoders • The proposed autoencoder outperforms conventional feature extraction methods • The autoencoder performance is dependent on hyperparameters and parameters selected • Results of hyperparameter optimization of the network are provided • The autoencoder based ROM shows high accuracies and reduced simulation times [ABSTRACT FROM AUTHOR]

Abstract: An energy model generated during the design phase of a building could - in principal - be converted into a calibrated energy model and used to improve the building’s operational performance. However, this rarely happens in practice. Through a survey of 306 building professionals, this research investigates whether this model reuse is technically feasible, based on today’s design-phase models, and what non-technical barriers might stand in the way of its implementation. An important finding is that 75% of the engineers/energy modelers surveyed believed that their models could be used by a third party in building commissioning and operation. At the same time, many modelers voiced various reservations that might prevent them from sharing their energy models with the owner or design team. These reservations varied from a desire to protect their intellectual property, to liability concerns, to the fear of incurring additional unpaid work. In response to these findings, this paper provides suggestions for overcoming these non-technical challenges and includes references for contract precedents. [Copyright &y& Elsevier]

Abstract: The paper discusses methods of selecting typical weather data and describes the selection of test reference years (TRYs) for Subang, Malaysia. The TRYs were selected using the Finkelstein–Schafer statistic, from 19 years of meteorological data recorded during the period 1980-98. Changing the weighting of the various meteorological variables (dry bulb temperature, solar radiation, relative humidity and wind speed) in the range appropriate for building performance studies had little effect on the selection, so it is recommended that equal weightings should be used unless there are particular reasons for using some other weighting. The replacement of relative humidity by moisture content had little effect on the selection. [Copyright &y& Elsevier]

Abstract: Empirical validation of building energy simulation codes is an important component in understanding the capacity and limitations of the software. Within the framework of Task 34/Annex 43 of the International Energy Agency (IEA), a series of experiments was performed in an outdoor test cell. The objective of these experiments was to provide a high-quality data set for code developers and modelers to validate their solar gain models for windows with and without shading devices. A description of the necessary specifications for modeling these experiments is provided in this paper, which includes information about the test site location, experimental setup, geometrical and thermophysical cell properties including estimated uncertainties. Computed overall thermal cell properties were confirmed by conducting a steady-state experiment without solar gains. A transient experiment, also without solar gains, and corresponding simulations from four different building energy simulation codes showed that the provided specifications result in accurate thermal cell modeling. A good foundation for the following experiments with solar gains was therefore accomplished. [Copyright &y& Elsevier]

Abstract: Building energy simulation programs compute numerical approximations to physical phenomena that can be modeled by a system of differential algebraic equations (DAE). For a large class of building energy analysis problems, one can prove that the DAE system has unique solution that is once continuously differentiable in the building design parameters. Consequently, if building simulation programs are built on models that satisfy the smoothness assumptions required to prove existence of a unique smooth solution, and if their numerical solvers allow controlling the approximation error, one can use such programs with generalized pattern search optimization algorithms that adaptively control the precision of the solutions of the DAE system. Those optimization algorithms construct sequences of iterates with stationary accumulation points and have been shown to yield a significant reduction in computation time compared to algorithms that use fixed precision cost function evaluations. In this paper, we state the required smoothness assumptions and present the theorems that state existence of a unique smooth solution of the DAE system. We present BuildOpt, a detailed thermal and daylighting building energy simulation program. We discuss examples that explain the smoothing techniques used in BuildOpt. We present numerical experiments that compare the computation time for an annual simulation with the smoothing techniques applied to different parts of the models. The experiments show that high precision approximate solutions can only be computed if smooth models are used. This is significant because today''s building simulation programs do not use such smoothing techniques and their solvers frequently fail to obtain a numerical solution if the solver tolerances are tight. We also present how BuildOpt''s approximate solutions converge to a smooth function as the precision parameter of the numerical solver is tightened. [Copyright &y& Elsevier]

Given that human behavior is increasingly recognized to play an essential role in building energy use, the validity of occupant-related assumptions in the building code and in the energy performance modeling tools used for demonstrating code compliance is a central concern. This paper investigates the current occupant-related assumptions in the National Building Code (NBC) of Canada compared to new and emerging data sources, along with their potential energy and design impacts. Six parameters were selected: setpoint temperatures, domestic hot water (DHW) use, appliances lighting and plug (ALP) loads, internal heat gains, mechanical ventilation, and the number of occupants. For each parameter, available data sources were compared against the NBC assumptions. Our analysis indicates that several code assumptions are substantially different from findings in recent measurement-based studies. For the temperature setpoints, the mean heating and cooling setpoints are approximately 1.5 °C cooler than the current NBC specification (21 °C and 25 °C, respectively). For DHW, the total daily volume is highly dependent on the number of occupants; hence, the current assumption of NBC (225 L/day) may not reflect the typical current use. Moreover, the hourly schedule for DHW use specified by NBC is significantly different than all data sources found (primarily in the timing and magnitude of morning and evening peaks in usage). For internal heat gains, the available data suggests a similar profile as NBC, except for that absence of a morning peak. Therefore, the findings presented in this study illustrate a need for revising the current building standards/codes. • A comprehensive review of new and emerging occupant-related data in Canada. • Occupant-related assumptions in NBC varies from the new available datasets. • Thermostat data from over 13,000 homes across Canada were analyzed. • The total daily volume of DHW use is highly dependent on the number of occupants. • A daily ALP loads profile from multiple sources was developed. [ABSTRACT FROM AUTHOR]