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Book - School of Science and Technology

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674 Running costs20%. The variables encountered <strong>and</strong> the assumptions made preclude a greater precision.The results however are valuable in a comparative sense <strong>and</strong> may be used with a muchimproved level <strong>of</strong> confidence when considering the relative merits <strong>of</strong> a variety <strong>of</strong> energysources: this is the application for which they are intended.Air-conditioned buildingsEnergy predictions for air-conditioned buildings take on a different dimension from thosefor heated only buildings. As well as heat energy, the designer is concerned with cooling,dehumidification <strong>and</strong> humidification <strong>and</strong> in consequence, simple averaged temperaturerelationships between inside <strong>and</strong> outside are no longer an adequate basis for calculation.It is necessary to take account <strong>of</strong> the coincident values for dry-bulb <strong>and</strong> wet-bulbtemperatures <strong>and</strong> <strong>of</strong> solar radiation; wind speed may be important but its variation withtime is not normally taken into account since it is less significant than the other parameters.It would be possible to make manual calculations but, for practical purposes, thesewould be limited to 12 average monthly conditions. However, averages over such longperiods may give misleading results since, for example, a month's average conditionsmay indicate no heating or cooling; a similar <strong>and</strong> equally important situation mayexist in respect <strong>of</strong> humidification <strong>and</strong> dehumidification. An improvement on thisapproach is the concept <strong>of</strong> b<strong>and</strong>ed weather data details <strong>of</strong> which are given in the1982 edition <strong>of</strong> Guide, Section A2 where the weather in each month is divided intogroups <strong>of</strong> days corresponding to ten equal intervals (b<strong>and</strong>s) for a given parameter.The calculation process, nevertheless, would be tedious <strong>and</strong> liable to error <strong>and</strong> wouldbe further complicated by the need to calculate not only the room heating <strong>and</strong>cooling, humidification <strong>and</strong> dehumidification loads for each set <strong>of</strong> conditions but alsothe corresponding plant psychrometric processes. B<strong>and</strong>ed weather data has beendropped from the current Guide A, but reference is retained here since the conceptmay have value for some applications.Such energy calculations can only be carried out with any accuracy <strong>and</strong> consistency bycomputer simulation methods, a science <strong>of</strong> modelling the behaviour <strong>of</strong> real systems. Thesetechniques enable the calculations to model the interactions between building fabric,particularly the influence <strong>of</strong> thermal mass on dynamic response, room heating <strong>and</strong> coolingloads, external climate, simultaneous psychrometric processes at the plant, controlmethods, plant efficiencies <strong>and</strong> other factors. In the program a logical mathematicaldescription may be prepared to account for all the significant variables, including useroperating patterns for intermittent or continuous occupancy, use <strong>of</strong> equipment <strong>and</strong>lighting. It should be recognised that such sophisticated programs require substantialamounts <strong>of</strong> data <strong>and</strong> that care is needed to ensure that the data are appropriate to thedesign issue which is being examined.For details reference should be made to CIBSE Application Manual, Building <strong>and</strong>Environmental Modelling, AM 11. Simulation programs would normally use weathertapes giving hour-by-hour values including dry bulb <strong>and</strong> wet bulb temperatures, solarradiation, wind speed <strong>and</strong> direction <strong>and</strong> illuminance.Since the early 1980s CIBSE has contributed to the development <strong>of</strong> annual weatherdata to establish a recognized representative 12 consecutive months data set for predictingenergy consumption. The early work produced Example Weather Year data for six sites,which was later extended to 15 sites. More recent work has produced improved sets <strong>of</strong>

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