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Examples include Knight et al (1991) who dealt with annual hourly solar data prediction (with recommendations for procedures to model dry-bulb temperature and relative humidity); Gul et al (1998) who cross-correlated solar data from other known data; Ren & Wright (2002) who proposed a hybrid deterministic-stochastic short time horizon predictor (24 hours) for solar and temperature data for use in, inter alia, adaptive controller development; van Paassen & Luo (2002) who develop a weather data prediction method for analysing climate change impact on buildings Though most of these methods lead to hourly data, the methodologies used are in most cases equally applicable to higher time resolution predictions subject to available weather data sets Procedures to describe the highly stochastic inputs due to building users are less well developed Various assumptions are made in practice-based patterns of discrete switching events.

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One possibility is to make use of monitoring data for lighting or small power demand as a proxy for mean user activity and, about which, a random component can be imposed (eg Jones & Underwood 2003) User activity will usually exhibit strong repeating sequence patterns de ned by three timescales; daily per week, weekly per month and monthly per year Data for modelling heat gains from equipment sources have been reported in several sources (Parsloe & Hejab 1992; Wilkins & Hosni 2000; Jones & Underwood 2003) Most of what is reported centres on nameplate ratio (ie actual power drawn by equipment divided by the nameplate power rating) comparisons of existing equipment and utilisation of equipment (ie diversity).

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Typical nameplate ratios were found to range from 25 to 50% and diversities from 37 to 78% with equipment loads of less than 20 W m-2 in many instances, and often considerably less than this Jones & Underwood (2003) additionally recommended procedures for pro ling the variation in power utilisation over typical seasonal days based on second-order least squares regression tting to half-day data sets By introducing a random component whose amplitude is xed by simulated maximum and minimum bounds on the data, a daily power pro le emerges (eg Fig 63) Regarding the fraction of equipment heat gain attributable to either radiant or convective emission (the so-called radiant-convective split ), Hosni et al (1998) evaluated this for a range of of ce and laboratory equipment, concluding that the fractions can vary considerably (11 61% radiant); and Liesen et al.

5. Select the desired data set from the Spry Data Set list. 6. If you have a current selection, choose either the Wrap Selection or Replace Selection option. 7. Click OK when you re done.

As you can with other plug-ins, you can pass other attributes to the Shockwave movie via the Parameters dialog box available by clicking the Parameters button on the Property Inspector. Press the add (+) button to begin inserting additional parameters. Enter the attributes in the left column and their respective values in the right. To remove an attribute, highlight it and select the delete ( ) button.

(1998) reported on the sensitivity of cooling load calculations to its variation..

books = req.getParameterVa1ues ( "book")

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2. Click a palette to open it in the Palette dialog box. You can edit and delete existing palettes from the Open Palette drop-down menu. You can create a new palette, too. Follow these steps: 1. From the Open Palette drop-down menu, select Edit Palette. 2. In the Palette dialog box, click the New Palette button. 3. Select RGB values, or click the Eyedropper button and drag the dropper over the color spectrum to create a color. 4. Once you create a color you want to add to your palette, click the Add button. 5. When you are done creating a new palette, click the Save button and enter a name for the palette. The new file is saved with a default .pal extension in the Palettes subdirectory.