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AbstractOutdoor design conditions are important parameters for energy efficiency of buildings. The result of incorrect selection of outdoordesign conditions can be dramatic in view of comfort and energy consumption. In this study, the influence of different outdoor designconditions on air conditioning systems is investigated. For this purpose, cooling loads and capacities of air conditioning equipments for asample building located in Adana, Turkey are calculated using different outdoor design conditions recommended by ASHRAE, the cur-rent design data used in Turkey and the daily maximum dry and wet bulb temperatures of July 21st, which is generally accepted as thedesign day. The cooling coil capacities obtained from the different outdoor design conditions considered in this study are compared witheach other. The cost analysis of air conditioning systems is also performed. It is seen that the selection of outdoor design conditions is avery critical step in calculation of the building cooling loads and design capacities of air conditioning equipments. 2007 Elsevier Ltd. All rights reserved.27896
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Keywords: Weather design data; HVAC system; Cooling load; Cost analysis
1. IntroductionLocal climatic conditions are important parametersfor the energy efficiency of buildings. Because the energyconsumption in buildings depends on climatic condi-tions and the performance of heating ventilating and airconditioning (HVAC) systems changes with them as well,better design in building HVAC applications that takeaccount of the right climatic conditions will result in bettercomfort and more energy efficient buildings.Outdoor design conditions are weather data informationfor design purposes showing the characteristic features ofthe climate at a particular location. They affect buildingloads and economical design. The result of incorrect selec-tion of outdoor conditions can be dramatic in view ofenergy and comfort. If some very conservative, extreme conditions are taken, uneconomic design and over sizingmay result. If design loads are underestimated, equipmentand system operation will be affected. However, selectingthe correct type of weather data is a difficult problem. Toovercome the problem, Yoshida and Terai [1] constructedan autoregressive moving average (ARMA) type weathermodel by applying a system identification technique tothe original weather data. Li et al. [2] studied climaticeffects on cooling load determination in subtropicalregions. They found that the outdoor climatic conditionsdeveloped for cooling load estimations are less stringentthan the current outdoor design data and approachesadopted by local architectural and engineering practices.Zogou and Stamatelos [3] provided a comparative discus-sion on the effect of climatic conditions on the design opti-mization of heat pump systems and showed that climaticconditions significantly affect the performance of heatpump systems, which should lead to markedly differentstrategies for domestic heating and cooling, if an optimiza-tion is sought on sustainability grounds. Lam [4] studied climatic influences on the energy performance of air condi-tioned buildings and found that the predictions of annualcooling loads, peak cooling loads and annual electricityconsumption differ by up to about 14%. Bulut et al. [5,6]determined new cooling and heating design data for Tur-key. They used the current outdoor design data locally usedand the new data presented in their studies [5,6] in order toevaluate the influence of the weather data set on the heat-ing and cooling load. They found up to 25% and 32% dif-ferences between the cases considered for cooling andheating loads, respectively.Outdoor design conditions corresponding to differentfrequency levels of probability for several locations in theUnited States and around the world are developed by theAmerican Society of Heating, Refrigeration and Air Con-ditioning Engineers, Inc. (ASHRAE) [7]. Weather dataincludes design values of dry bulb temperature with meancoincident wet bulb temperature, design wet bulb tempera-ture with mean coincident dry bulb temperature and designdew point temperature with mean coincident dry bulb tem-perature and corresponding humidity ratio. These designdata are the outdoor conditions that are exceeded duringa specified percentage of time. Warm season temperatureand humidity conditions correspond to annual percentilevalues of 0.4, 1.0 and 2.0. Cold-season conditions are basedon annual percentiles of 99.6 and 99.0. The 0.4%, 1.0% and2.0% annual values of occurrence represent the value thatoccurs or is exceeded for a total of 35 h, 88 h and 175 h,respectively, on average, every year, over the period ofrecord. The selection of frequency as risk level in designconditions depends on the applications. Representing theclimatic design data for several frequencies of occurrencewill also enable designers to consider various operationalpeak conditions.
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