Daylighting simulation in the DOE-2 building energy analysis program

A daylighting calculation has been intergrated grated into the DOE-2 building energy analysis computer program. Users can, for the first time in a widely-accepted, publicly-available program, determine the impact of daylight utilization on heating and cooling loads, energy use, energy cost, and peak electrical demand. We describe the algorithms which simulate hourly-varying interior illuminance, management of windows for sun and glare control, and the operation of electric lighting control systems. Sample DOE-2 daylighting output reports are presented and results of program validation against scale model illuminance measurements using the Lawrence Berkeley Laboratory sky simulator are discussed.     

A study of the daylighting performance and energy use in heavily obstructed residential buildings via computer simulation techniques

The quality and quantity of natural light entering a building depends on both internal and external factors. In Hong Kong, many buildings are high-rise blocks constructed close to each other and hence the external factor plays a significant role in daylighting designs. This paper studies the daylighting performance and energy use for residential flats facing large sky obstructions via computer simulations. Key building parameters affecting daylighting designs are presented. The daylighting performance for typical interior rooms was investigated in terms of illuminance level and daylight factor. The daylight levels of residential flats can be severely reduced by neighboring buildings and hence the externally reflected component would be the main source of natural light. The indoor daylight levels for kitchen and living/dining faced large neighboring building were found always less than the standard maintenance illuminance during daytime period. These imply that many residential flats in Hong Kong would have to rely on supplementary electric lighting.     

An ANN-based model for the prediction of internal lighting conditions and user actions in non-residential buildings

This paper presents an Artificial Neural Network (ANN) based approach able to predict the internal lighting conditions in a working environment, taking into account the daylight entering the respective space as well as the special requirements of each user. The model training procedure is based both on real illuminance and occupancy data (measurements throughout a year) and on simulations, in order to integrate all possible conditions. User preferences in respect to lighting and blinds are expressed through probability curves. Illuminance due to the external daylight is calculated and predicted throughout the whole year, depending on the weather conditions, the time of the day, the location and the office orientation. The work plane distance from the window and the usage of blinds are also considered. The proposed model is further implemented for the prediction and evaluation of energy consumption for lighting in a working space based on the user preferences.     

Determination of vertical daylight illuminance under non-overcast sky conditions

In a dense urban region in which high-rise buildings are packed inside limited land areas, the daylight components reflected from ground and surrounding buildings play significant roles in daylighting design. The natural light available in an interior strongly depends on the amount of daylight reaching the window facades. Lately we proposed a calculation procedure, presented in form of simple mathematical expressions and diagrams, to determine the daylight illuminance on a vertical plane under overcast skies. This paper extends the study to non-overcast sky conditions. The performance of the proposed method was evaluated by comparison with the daylight illuminance simulated by a lighting software, namely RADIANCE, and with measurements under real skies. It was found that the data estimated by the proposed method were in good agreement both with the values simulated by RADIANCE and with the measured results. The paper offers to architects and building engineers a useful tool for estimating the daylight illuminance and in particular for determining and assessing various daylighting schemes and concepts during design and construction stages.     

Evaluation of lighting performance in office buildings with daylighting controls

Lighting control integrated with daylighting is recognised as an important and useful strategy in energy-efficient building designs and operations. It is believed that proper daylighting schemes can help reduce the electrical demand and contribute to achieving environmentally sustainable building development. This paper presents field measurements on daylighting for a fully air-conditioned office building in Hong Kong. Electricity consumption by the fluorescent luminaires, indoor illuminance levels and the room parameters affecting daylighting designs were recorded and analysed. The measurements covered several cellular offices facing opposite orientations with and without daylighting controls. The findings suggest that daylighting schemes can result in substantial energy savings in air-conditioned office buildings in Hong Kong. Results are presented and the design implications are discussed.     

A generative facade design method based on daylighting performance goals

Successful daylighting design is a complex task which requires the designer to consider numerous design elements and their effects on multiple performance criteria. Facades, in particular, include many variables which may dramatically impact daylighting performance. Genetic algorithms (GAs) are optimization methods which are suitable for searching large solution spaces, such as those presented by design problems. This article presents a GA-based tool which facilitates the exploration of facade designs generated based on illuminance and/or glare objectives. The method allows the user to input an original 3d massing model and performance goals. The overall building form remains the same while facade elements may change. Ten parameters are considered, including materials and geometry of apertures and shading devices. A simple building data model is used to automatically generate a 3d model of each solution. Results from single- and multi-objective case studies are presented to demonstrate a successful goal-driven design exploration process.     

用太阳辐射转化法计算室外照度值

针对中国光气候数据资料不足、太阳辐射数据丰富以及资金有限的现状,在分析多种光气候数据采集方法的基础上,选用太阳辐射转化法获得光气候数据.引入发光功效的概念作为室外照度值和太阳辐射量转化的基本关系,并分析对比了已有的发光功效模型,运用天空晴朗指数,以重庆1991-1992年的实测数据为基础进行拟合,建立了总发光功效模型和...     

Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input–output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input–output “meta-models”. The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments.     

Illuminance-based slat angle selection model for automated control of split blinds

Venetian blinds play an important role in controlling daylight in buildings. Automated blinds overcome some limitations of manual blinds; however, the existing automated systems mainly control the direct solar radiation and glare and cannot be used for controlling innovative blind systems such as split blinds. This research developed an Illuminance-based Slat Angle Selection (ISAS) model that predicts the optimum slat angles of split blinds to achieve the designed indoor illuminance. The model was constructed based on a series of multi-layer feed-forward artificial neural networks (ANNs). The illuminance values at the sensor points used to develop the ANNs were obtained by the software EnergyPlus™. The weather determinants (such as horizontal illuminance and sun angles) were used as the input variables for the ANNs. The illuminance level at a sensor point was the output variable for the ANNs. The ISAS model was validated by evaluating the errors in the calculation of the: 1) illuminance and 2) optimum slat angles. The validation results showed that the power of the ISAS model to predict illuminance was 94.7% while its power to calculate the optimum slat angles was 98.5%. For about 90% of time in the year, the illuminance percentage errors were less than 10%, and the percentage errors in calculating the optimum slat angles were less than 5%. This research offers a new approach for the automated control of split blinds and a guide for future research to utilize the adaptive nature of ANNs to develop a more practical and applicable blind control system.     

基于气象热舒适度的建筑能耗灰色神经网络预测

针对建筑能耗受局地气候多因素影响的特点,为了客观准确地对建筑能耗进行预测,本文引入了气象热舒适度来综合分析气候对建筑能耗的影响,并以该指数预测值、建筑能耗原始数据和日期类型作为输入层,进行复合灰色神经网络模型预测建筑能耗。该方法不仅克服了灰色模型和神经网络存在的预测缺陷,同时还考虑了气象因素对建筑能耗的影响。通过对北京某大厦的实例应用分析,取得了较高精度的预测结果,证实了该方法的合理可靠,为建筑能耗预测提供了新途径,其预测结果也将为大型建筑空调系统的再优化设计和改造提供参考。     

气凝胶玻璃在东北地区建筑应用的采光及能耗模拟研究

采用Ecotect Analysis 2015和DeST软件,探究气凝胶玻璃作为围护结构对东北地区建筑的采光及建筑能耗的影响,模拟地址选取沈阳,模拟时间为1年,模拟模型为普通二层办公建筑。结果显示,光学性能方面,气凝胶玻璃应用后的可见光照度均能达到标准最低要求495lx,室内最低也达到540lx,天棚玻璃光线直射处的可见光照度降低了1419.3lx,避免了眩光;热学性能方面,相较于同样厚度的传统中空玻璃系统,使用气凝胶玻璃模拟的室内全年冷负荷量降低了10.65个百分点。研究发现,气凝胶玻璃作为建筑外围护结构时,可保证建筑应有的采光效果,在提高采光舒适度的前提下还能有效降低建筑能耗。     

Lighting preferences in office spaces concerning the indoor thermal environment

The accurate prediction of the visual comfort zone in an indoor environment is difficult as it depends on many parameters. This is especially the case for large compact urban areas in which the density and shadow from neighboring buildings can limit the accessible daylighting in indoor spaces. This paper investigates the satisfaction range for illuminance regarding indoor air temperature in office buildings and the significant parameters affecting this range in six office buildings in Tehran, Iran. Lighting comfort has been evaluated by a subjective survey (509 total responses) and field measurement. The questionnaires were filled out in 146 and 109 rooms in summer and winter, respectively. The results show that the illuminance should not be less than 550 lx, while illuminance between 600 and 650 lx provides the highest satisfaction level. The satisfaction with lighting level is affected by individual parameters such as age, type of activity, and environmental parameters such as window orientation, external obscurations, and season. A relationship was observed between lighting level satisfaction and thermal condition acceptance, and the overall comfort depends more on thermal conditions than the lighting level.     

Development of a computational tool to quantify architectural-design effects on thermal comfort in naturally ventilated rural houses

In the present study, the effect of the opening size and building direction on night hours thermal comfort in a naturally ventilated rural house is investigated. Initially, the airflow in and around the building is simulated using a validated computational fluid dynamics (CFD) model. Local climate night-time data (wind velocity and direction, temperature and relative humidity) are recorded in a weather station and the prevailing conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are then used to evaluate indoor thermal comfort. For this reason, special thermal comfort indices, i.e. the well-known predicted mean vote (PMV) index and its modifications especially for natural ventilation, are calculated with respect to various residential activities. Mean values of these indices (output variables) within the occupied zone are calculated for different combinations of opening sizes and building directions (input variables), to generate a database of input–output pairs. Finally, the database is used to train and validate Radial Basis Function Artificial Neural Network (RBF ANN) input–output “meta-models”. It is demonstrated that the proposed methodology leads to reliable thermal comfort predictions, while the optimum design variables are easily recognized.     

An analysis of measured and simulated daylight illuminance and lighting savings in a daylit corridor

Lighting control integrated with daylighting is recognised as an important and useful strategy in energy-efficient building designs and operations. Prediction of the internal daylight levels is a key stage in daylighting designs. With the advances in computer technology, the computation of daylight illuminances can be conducted via lighting simulation program. This paper presents a study of the daylight coefficient (DC) approach using RADIANCE lighting software in simulating the indoor daylight illuminance of a corridor. The interior daylight illuminance data measured in the corridor were compared with the simulated results based on the computer software. It was found that the DC approach could give satisfactory results especially for the sun-shaded surface and sun-facing surface receiving a large amount of direct sunlight. Further, the daylight illuminance detected by the photosensor was also simulated in conjunction with measured daylight illuminance, dimming ratio and electric lighting power to predict the lighting energy savings. The findings suggested that the measured and predicted data showed a good agreement when large electric lighting savings resulted. The probable reasons causing the discrepancies were discussed.     

Dynamic wind response of tall buildings using artificial neural network

This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.     

教学建筑侧窗自然采光策略

教学建筑是一类对光环境要求比较苛刻的建筑。通过分析教学建筑对光环境的特殊要求,指出教学建筑自然采光概念包含两个层面的内容：采光和遮光。对几种简单易行的侧窗自然采光方式进行了述评,并通过软件模拟与常规的侧窗采光方式进行比较,从而显示出其优势。     

Computational fluid dynamics and artificial neural network-based analysis and forecasting of wind effects on obliquely parallel multiple building models using categorical variable encoding

This research investigates the influence of wind on four closely spaced parallel building models using computational fluid dynamics (CFD). The buildings are positioned either perpendicular to the wind direction or at various oblique angles. The aerodynamic results obtained for these buildings in an interfering condition are compared to those of an isolated tall building using the interference and obliquity effect (IOE) factor. Graphical comparisons are made among the different models and faces, considering various obliquity angles (OAs). The inner building models exhibit higher pressure and force coefficients at higher OAs. The variation of pressure coefficients along the horizontal peripheral direction is also analyzed, and the trade-offs of higher and lower OAs are discussed for the different building models. Furthermore, an artificial neural network (ANN) is trained using surface pressure coefficients from approximately 6000 data points distributed over different facets of building models. Categorical encoding is employed using one-hot encoding-based dummy variables for different building models, while numerical variables such as OA and X, Y, and Z coordinates are included as input for the ANN. The ANN is trained using a total of 238,340 data points (considering different building models and different OA scenarios), and its parameters are monitored during training to minimize errors and achieve high predictability. Finally, a representative case is used to plot the pressure contour obtained from the trained ANN, which is shown to be highly comparable to the CFD-based contour.     

Development of an optimal daylighting controller

A new lighting and daylighting control strategy is modeled and evaluated against conventional lighting and daylighting controls. The new lighting and daylighting control strategy can be incorporated in an energy management and control system (EMCS) to operate and control lighting fixtures in any indoor space. The new daylighting control can also be modeled and integrated in detailed building energy simulation tools. Through a validation analysis, it was found that the new control strategy provides more energy savings than conventional daylighting controls. Moreover, the validation analysis has indicated that existing daylighting control simulation analysis tools could overestimate lighting energy savings associated with daylighting controls. Moreover, it was also found that if calculated solar and illuminance data are used instead of measured solar radiation data, the errors in predicting lighting energy use when daylighting controls are utilized can be significant.     

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24 h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy.     

A validation of the Radiance three-phase simulation method for modelling annual daylight performance of optically complex fenestration systems

A new capability that enables annual simulation of optically complex fenestration systems has been added to Radiance. The method relies on bidirectional scattering distribution function (BSDF) input data, which are used in an efficient matrix calculation to compute time-step performance given TMY data. The objective of this study was to explain the value of this capability to designers and developers of innovative daylighting systems and to demonstrate its speed and accuracy via comparisons of simulated to measured illuminance data for a daylight-redirecting optical louver system. The method was shown to provide valid results that accurately replicate real-world conditions with an absolute mean bias error below 13% and a root mean square error below 23%. Routine application of this new capability will not be hindered by slow computational speed for illuminance calculations. Instead, the capability will be dependent on the availability of BSDF data for daylighting, shading and fenestration systems.