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The objective of this document is to promote the use of dynamic daylight performance measures for sustainable building design. The paper initially explores the shortcomings of conventional, static daylight performance metrics which concentrate on individual sky conditions, such as the common daylight factor. It then provides a review of previously suggested dynamic daylight performance metrics, discussing the capability of these metrics to lead to superior daylighting designs and their accessibility to nonsimulation experts. Several example offices are examined to demonstrate the benefit of basing design decisions on dynamic performance metrics as opposed to the daylight factor.

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 39kWh/m 2 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.

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1016/j.enbuild. 2006.03.012 Energy and Buildings, 38, 7, pp. 824-835, 2006-07-01 Findings from a survey on the current use of daylight simulations in building design Reinhart, C. F.; Fitz, A. 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.91% 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.

In this paper we present, demonstrate and validate a method for predicting city-wide electricity gains from photovoltaic panels based on detailed geometric 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 neareby 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 to 5.3% of measured production when calibrating for measured weather data. 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 ten buildings selected randomly from the Cambridge dataset.

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