A dimensionality reduction method to select the most representative daylight illuminance distributions

One challenge when evaluating daylight distribution is dealing with the large amount of temporal and spatial data, visualisations and variability in illuminances that are assessed in buildings. Using a dimensionality reduction method based on principal component analysis, we identified the most representative annual daylight distributions. We modelled a rectangular room containing an analysis grid of 3200 illuminance sensor points and simulated 3285 different temporal daylight conditions using an annual occupancy schedule ranging from 08:00 to 17:00 with one-hour sampling intervals in two locations: Singapore and Oakland, California. Our approach explained 98 % of the illuminance variability with three daylight distributions in Singapore, and 92 % using six in Oakland, California. Our dimensionality reduction strategy was also generalised using a complex building geometry showing the utility of the method. We think this approach can be used to provide a more efficient and reliable method to analyse daylight performance in building practice

The use of visual comfort metrics in the design of daylit spaces

Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (pages 137-144).It is desirable to design buildings with natural daylight and views to the outside, which can maximize passive solar heating, minimize electric lighting use and contribute to feelings of wellbeing and awareness. Unfortunately, the presence of daylight is not always a positive one. Excessive brightness, strong contrast or intense reflections from daylight can cause visual discomfort or the inability to perform tasks. Typically, the total amount of luminous flux incident upon a surface per unit area - illuminance - present is used to predict visual discomfort due to questions about the benefit and validity of luminance-based analysis measures that are more related to the way the human visual system perceives light. This thesis aims to advance the understanding and usefulness of visual comfort prediction to the point that it can become commonly used in architectural design processes. One method through which this is achieved is by testing the ability of visual comfort analysis to resolve subjective occupant comfort. It was found that of existing discomfort glare metrics, daylight glare probability (DGP) was the most likely to perform well in a variety of daylight conditions and space types. Furthermore, a long-term simulation and survey study found that between 53.7% and 70.1% of an occupant's visual satisfaction could be resolved by analyzing DGP, the presence of direct sunlight and predicted monitor contrast ratio. This choice of metrics was reinforced by a separate laboratory study, which found that 74.4% of subjective comfort could be resolved and identified new subjective luminance thresholds that identify likely discomfort. A new adaptive visual comfort model, the 'adaptive zone,' is proposed in this thesis to deal with spatiality and view in visual discomfort analysis. Finally, ways of applying these verified and new measures in design processes are tackled in this work by producing new temporal maps, spatial discomfort analysis, and plan-based mappings of visual satisfaction.by John Alstan Jakubiec.Ph. D. in Architecture: Building Technolog

Towards Subjectivity in Annual Climate-Based Daylight Metrics

This paper presents a post-occupancy study of 543 participants in 10 daylit office buildings in Singapore. Calibrated daylighting and electric lighting simulation models of each building were created and verified. HDR photographs and vertical and horizontal illuminance measurements were taken at each participant’s workspace, and a survey on their long-term and instantaneous subjective evaluations of lighting were collected. For the first time, this study compares climate-based daylighting metrics (CBDM’s) to occupant’s longterm subjective impressions. The authors find that simulated mean annual horizontal illuminance correlates strongly with occupants’ satisfaction with access to daylight. 50% occupant satisfaction with daylight begins at levels as low as 80 lx, far below current lighting sufficiency standards. Vertical illuminance measures did not exhibit strong correlations with reported discomfort. These results are an initial investigation of CBDM’s use for more than lighting sufficiency and illustrate the need for further study of overlighting metrics

Building Climate-based Daylighting Models Based On One-time Field Measurements

Calibrated climate-based lighting simulation models of buildings perform an essential role in post-occupancy evaluations (POE), such as annual frequency assessments of daylighting quality and visual discomfort. However, the role of lighting analysis is temporally limited by instantaneous measurements or limited in scale by requiring constant monitoring of occupied spaces with expensive sensors. Building calibrated models is thus challenging due to limited information, short durations of access, the concurrent presence of electric lighting and daylighting, and transient usage of dynamic shades of occupied spaces. In this paper, the authors present a calibration process to build annual daylighting and electric lighting simulation models based on one-time field measurements, exemplified through a dataset of 540 individual office desks across 10 office spaces. The authors calibrated lighting models to be reliable enough for assessing the relationship of annualized climate-based daylighting metrics (CBDMs) to participants long-term perceptions of lighting quality. The proposed process to build calibrated climate-based models for POE’s based on one-time field measurements at each building is validated through comparing measured and simulated illuminance data at every work desk and results are sufficiently positive with logarithmic relative RMSE values of 4.3% and 6.8% and relative RMSE values of 25.8% and 45.5% for horizontal and vertical illuminances respectively. Vertical illuminance was found to vary more with measured data due to the uncertainty of monitor screen luminances. This paper demonstrates that measured data through onetime visits can be utilized to build reliable calibrated lighting simulation models to integrate long-term annual lighting results in post-occupancy evaluations

A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations

In this paper we present, demonstrate and validate a method for predicting city-wide electricity gains from photovoltaic panels based on detailed 3D urban massing models combined with Daysim-based hourly irradiation simulations, typical meteorological year climactic data and hourly calculated rooftop temperatures. The resulting data can be combined with online mapping technologies and search engines as well as a financial module that provides building owners interested in installing a photovoltaic system on their rooftop with meaningful data regarding spatial placement, system size, installation costs and financial payback. As a proof of concept, a photovoltaic potential map for the City of Cambridge, Massachusetts, USA, consisting of over 17,000 rooftops has been implemented as of September 2012.The new method constitutes the first linking of increasingly available GIS and LiDAR urban datasets with the validated building performance simulation engine Daysim, thus-far used primarily at the scale of individual buildings or small urban neighborhoods. A comparison of the new method with its predecessors reveals significant benefits as it produces hourly point irradiation data, supports better geometric accuracy, considers reflections from near by urban context and uses predicted rooftop temperatures to calculate hourly PV efficiency. A validation study of measured and simulated electricity yields from two rooftop PV installations in Cambridge shows that the new method is able to predict annual electricity gains within 3.6-5.3% of measured production when calibrating for actual weather data and detailed PV panel geometry. This predicted annual error using the new method is shown to be less than the variance which can be expected from climactic variation between years. Furthermore, because the new method generates hourly data, it can be applied to peak load mitigation studies at the urban level. This study also compares predicted monthly energy yields using the new method to those of preceding methods for the two validated test installations and on an annual basis for 10 buildings selected randomly from the Cambridge dataset

Calibration and Validation of Climate-Based Daylighting Models Based on One-Time Field Measurements: Office Buildings in the Tropics

Calibrated climate-based lighting simulation models of buildings have the capacity to perform an essential role in postoccupancy evaluations, such as annual frequency assessments of daylighting quality and visual discomfort. However, in most postoccupancy case studies the role of lighting analysis is temporally limited by instantaneous measurements or limited in scale by requiring constant monitoring with expensive sensors. It is challenging to build calibrated models based on point-in-time measurements due to the presence of electric lighting, transient use of dynamic shades, limited information on the material specifications, and short durations of accessibility to the spaces being studied. The authors propose and present a calibration process for annual daylighting and electric lighting simulation models based on one-time field measurements of large daylit and electrically lit spaces exemplified through a data set of 540 individual office desks across 10 office spaces. The calibration process includes measuring lighting, physical, and material data during a one-time visit that are used to calibrate high dynamic range images and lighting simulation models using actual weather data. The calibration accuracy is validated based on measured and simulated luminance and illuminance data. Comparing measured and simulated illuminance, relative root mean squared error (RMSE) values were 25.8% and 45.5% for horizontal and vertical measurements, respectively. When tracking errors using log10(illuminance), approximating human perceptual differences, errors of 4.3% and 6.8% were achieved. Vertical illuminance was found to vary more with measured data due to the uncertainty of monitor screen luminances. The authors aim to achieve calibrated lighting models that are reliable enough to be used in assessing the relationship of annualized lighting metrics to participants' long-term perceptions of lighting quality, thereby enabling simulation models to be used in the postoccupancy evaluation process of building lighting. This article demonstrates that measured data through one-time visits can be utilized to build reliable calibrated lighting simulation models to integrate long-term annual lighting results in postoccupancy evaluations

Towards Subjectivity in Annual Climate-Based Daylight Metrics

This paper presents a post-occupancy study of 543 participants in 10 daylit office buildings in Singapore. Calibrated daylighting and electric lighting simulation models of each building were created and verified. HDR photographs and vertical and horizontal illuminance measurements were taken at each participant’s workspace, and a survey on their long-term and instantaneous subjective evaluations of lighting were collected. For the first time, this study compares climate-based daylighting metrics (CBDM’s) to occupant’s longterm subjective impressions. The authors find that simulated mean annual horizontal illuminance correlates strongly with occupants’ satisfaction with access to daylight. 50% occupant satisfaction with daylight begins at levels as low as 80 lx, far below current lighting sufficiency standards. Vertical illuminance measures did not exhibit strong correlations with reported discomfort. These results are an initial investigation of CBDM’s use for more than lighting sufficiency and illustrate the need for further study of overlighting metrics

A dimensionality reduction method to select the most representative daylight illuminance distributions

One challenge when evaluating daylight distribution is dealing with the large amount of temporal and spatial data, visualisations and variability in illuminances that are assessed in buildings. Using a dimensionality reduction method based on principal component analysis, we identified the most representative annual daylight distributions. We modelled a rectangular room containing an analysis grid of 3200 illuminance sensor points and simulated 3285 different temporal daylight conditions using an annual occupancy schedule ranging from 08:00 to 17:00 with one-hour sampling intervals in two locations: Singapore and Oakland, California. Our approach explained 98 % of the illuminance variability with three daylight distributions in Singapore, and 92 % using six in Oakland, California. Our dimensionality reduction strategy was also generalised using a complex building geometry showing the utility of the method. We think this approach can be used to provide a more efficient and reliable method to analyse daylight performance in building practice

Image-based Material Characterization for Daylight Simulation Using Illuminance-proxy and Artificial Neural Networks

A key aspect of daylight modeling is the definition of material optical properties. Characterization of such properties in existing indoor spaces with current methods is a labour-intensive and time-consuming task, especially in surfaces with considerable visual complexity. Faster and more accurate estimations of such properties will lead to more efficient workflows. Towards this direction, the present work studied the feasibility of using two novel approaches i.e. illuminance-proxy and probabilistic image based material characterization methods for implementation in daylight modeling. These approaches are compared with two common techniques, namely the manual selection from a measured dataset and the use of illuminance/luminance measurements. According to the results, both novel techniques are able to predict spatiallyaveraged Daylight Autonomy, continuous Daylight Autonomy, and Useful Daylight Illuminance in 300-3000 lx range with less than 5% errorGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Building Physic