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Abstract: This study describes the development and validation of a Radiance model for a translucent panel. Using goniophotometer data combined with integrating sphere measurements, optical properties of the panel were derived and converted into a Radiance model using the trans and transdata material types. The Radiance model was validated in a full-scale test room with a facade featuring the translucent panel material. Over 120,000 desktop and ceiling illuminances under 24,000 sky conditions were measured and compared to simulation results using the Perez sky model and a Radiance-based daylight coefficient approach. Overall mean bias errors (MBE) below 9% and root mean square errors (RMSE) below 19% demonstrate that translucent materials can be modeled in Radiance with an even higher accuracy than was demonstrated in earlier validation studies for the plastic, metal, and glass material types. Further analysis of results suggests that the accuracy of around ±20% currently reached by dynamic Radiance/Perez/daylight coefficient calculations for many material types is sufficient for practical design considerations. A procedure is described showing how goniophotometer and integrating sphere measurements can be used to accurately model arbitrary translucent materials in Radiance using transdata function files. [Copyright &y& Elsevier]

Abstract: This paper presents findings from a web-based survey on the current use of daylight simulations in building design. The survey was administered from December 2nd 2003 to January 19th 2004. One hundred and eighty five individuals from 27 countries completed the survey. The majority of respondents worked in Canada (20%), the United States (20%), and Germany (12%). Most participants were recruited through building simulation mailing lists. Their self-reported professions ranged from energy consultants and engineers (38%) to architects and lighting designers (31%) as well as researchers (23%). They worked predominantly on large and small offices and schools. Ninety one percent of respondents included daylighting aspects in their building design. Those who did not consider daylighting blamed lack of information and unwillingness of clients to pay for this extra service. Among those participants who were considering daylighting 79% used computer simulations. This strong sample bias towards computer simulations reflects that many participants had been recruited through building simulation mailing lists. Participants named tools’ complexity and insufficient program documentation as weaknesses of existing programs. Self-training was the most common training method for daylight simulation tools. Tool usage was significantly higher during design development than during schematic design. Most survey participants used daylighting software for parameter studies and presented the results to their clients as a basis for design decisions. While daylight factor and interior illuminances were the most commonly calculated simulation outputs, shading type and control were the most common design aspects influenced by a daylighting analysis. The use of scale model measurements had rapidly fallen compared to a 1994 survey, whereas, trust in the reliability of daylighting tools has risen. While participants named a total of 42 different daylight simulation programs that they routinely used, over 50% of program selections were for tools that use the RADIANCE simulation engine, revealing the program''s predominance within the daylight simulation community. [Copyright &y& Elsevier]

Abstract: Behavioural models derived from on-going field studies can provide the basis for predicting personal action taken to adjust lighting levels, remedy direct glare, and save energy in response to physical conditions. Enabling these behavioural models in advanced lighting simulation programs, such as DAYSIM and the Lightswitch Wizard, allows for a more realistic estimate of lighting use under dynamic conditions. The current downside of these approaches is that the whole building energy impact of manual changes in blind settings and lighting use, including its effect on heating and cooling requirements, is not considered. A sub-hourly occupancy-based control model (SHOCC), which enables advanced behavioural models within whole building energy simulation, is presented. The considered behavioural models are the Lightswitch2002 algorithms for manual and automated light and blind control, while the investigated whole building energy simulation program is ESP-r. The enhanced functionality is demonstrated through annual energy simulations aiming at quantifying the total energy impact of manual control over lights and window blinds. Results show that building occupants that actively seek daylighting rather than systematically relying on artificial lighting can reduce overall primary energy expenditure by more than 40%, when compared to occupants who rely on constant artificial lighting. This underlines the importance of defining suitable reference cases for benchmarking the performance of automated lighting controls. Results also show that, depending on the proportion of buildings occupants that actively seek out daylighting, reduced lighting use through automated control may not always produce anticipated savings in primary energy for indoor climate control. In some cases, reduced lighting use is shown to even increase primary energy expenditure for indoor climate control, trimming down initial primary energy savings in lighting alone. This reveals the superiority of integrated design approaches over simpler daylighting guidelines or rules of thumb. [Copyright &y& Elsevier]

A simulation algorithm is proposed that predicts the lighting energy performance of manually and automatically controlled electric lighting and blind systems in private and two-person offices. Algorithm inputs are annual profiles of user occupancy and work plane illuminances. These two inputs are combined with probabilistic switching patterns, which have been derived from field data, in order to predict the status of the electric lighting and blinds throughout the year. The model features four different user types to mimic variation in control behavior between different occupants.An example application in a private office with a southern facade yields that––depending on the user type––the electric lighting energy demand for a manually controlled electric lighting and blind system ranges from 10 to 39 kW h/m2 yr. The predicted mean energy savings of a switch-off occupancy sensor in the example office are 20%. Depending on how reliably occupants switch off a dimmed lighting system, mean electric lighting energy savings due to a daylight-linked photocell control range from 60% to zero. [Copyright &y& Elsevier]