energies

Article Heating and Cooling Degree-Days Maps of Pakistan

Khuram Pervez Amber 1,*, Muhammad Waqar Aslam 2, Faraz Ikram 1, Anila Kousar 3, Hafiz Muhammad Ali 4, Naveed Akram 1, Kamran Afzal 1 and Haroon Mushtaq 1 1 Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan; [email protected] (F.I.); [email protected] (N.A.); [email protected] (K.A.); [email protected] (H.M.) 2 Department of Computer System Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan; [email protected] 3 Department of Electrical (Power) Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan; [email protected] 4 Department of Mechanical Engineering, University of Engineering and Technology (UET), Taxila 47080, Pakistan; [email protected] * Correspondence: [email protected]; Tel.: +92-341-596-4460

Received: 7 December 2017; Accepted: 29 December 2017; Published: 2 January 2018

Abstract: The building sector consumes about 40% of the world’s primary energy. Seasonal climatic conditions have a significant effect on the energy consumption in buildings. One of the famous methods used for decoding this seasonal variation in buildings energy consumption is the “Degree Days Method”. Data has been widely published for the heating and cooling degree days of different countries. Unfortunately, there is very limited and outdated published data for the heating and cooling degree-days of Pakistan. In this study, yearly average heating and cooling degree-days for different regions of Pakistan are established by using 30 year long-term measured data for different base temperatures. The data is presented in tables and figures whereas heating and cooling degree-day maps of Pakistan have been developed.

Keywords: degree days; Pakistan; energy consumption; forecasting

1. Introduction The building sector consumes nearly 40% of the world’s primary energy [1] and is responsible for a significant amount of CO2 emissions which is a major cause of global warming [2]. With the increasing population and increasing global temperature, energy demand in the building sector is increasing day by day. To meet this growing energy demand and to ensure the effective usage of diminishing fossil fuel resources, mitigation measures must be implemented and practiced. In parallel it is also important to analyze historical energy consumption trends and to forecast the same for different climatic regions of the world in order to facilitate the policy makers in the development of effective policies concerning buildings energy performance. Outdoor temperature which is different in different parts of the world has a significant effect on the buildings cooling and heating energy demands. There are different methods for analyzing energy consumption patterns of buildings, yet, the Degree-days (DDs) method, invented by Lt-Gen. Sir Richard Strachey in 1878, is a simple and reliable method to establish energy analysis of buildings [3]. Degree-days represents a versatile climatic indicator which is commonly used in the analysis of building energy performance, as an e.g., (i) to perform energetic assessment of existent and new buildings; (ii) to analyze the territory energy consumption; (iii) to develop scenario analyses in terms of energy consumption forecasting, and so on. DDs can also be used to plan future energy targets and energy budgets, identify variations in consumption patterns and investigate building’s performance against typical benchmarks and most importantly in the preparation of policies concerning buildings’ energy consumption [4].

Energies 2018, 11, 94; doi:10.3390/en11010094 www.mdpi.com/journal/energies Energies 2018, 11, 94 2 of 12

Technically, Degree-days are nothing but the summation of the differences of temperatures for a specific time period [5]. The temperature difference is determined between mean ambient temperature and a reference temperature, usually known as base temperature or balance point temperature. The base temperature is a constant value at which cooling (heating) systems are not required to operate to maintain comfort conditions in the buildings. Selecting an optimum base temperature for the heating and cooling plants is crucial in order to optimize the performance of HVAC plants and to control HVAC related costs in buildings. For example, in USA, UK, and Germany, commonly used ◦ ◦ ◦ base temperaturesEnergies 2018 for, 11 the, 94 purpose of calculating HDDs and CDDs are 18.3 C, 15.5 2C of 12 and 15 C[6]. A number of researchers have established the HDDs and CDDs of different countries, e.g., Orhan etimportantly al. [7] established in the preparation HDDs of policies and CDDs concerning for buildings 78 different’ energy citiesconsumption of Turkey [4]. Technically, usingthe historical Degree-days are nothing but the summation of the differences of temperatures for a specific time temperature dataperiod from [5]. The the temperature relevant difference weather is stations. determined In between a similar mean study, ambient Altan temperature [3] established and a HDDs and CDDs forreference five different temperature, base usually temperatures known as base for temperature 79 city centres or balance of point Turkey temperature. using longThe base term historical data of 21 yearstemperature. Al-Hadhrami is a constant [8] value used at temperature which cooling (heating) data from systems 38 metrologicalare not required stations to operate and to established maintain comfort conditions in the buildings. Selecting an optimum base temperature for the heating ◦ HDDs and CDDsand cooling for different plants is crucial parts in of order Saudi to optimize Arabia the for performance a base temperature of HVAC plants of and 18.3 to controlC. Mattia et al. [4] determined HDDsHVAC related and CDDs costs in for buildings. Italy forFor 75example, different in USA, locations UK, and Germany for three, commonly different used base base temperatures. temperatures for the purpose of calculating HDDs and CDDs are 18.3 °C, 15.5 °C and 15 °C [6]. Viorel and ZamfirA [number9] established of researchers HDDs have andestablished CDDs the for HDDs 29 anddifferent CDDs of cities different of Romania countries, e.g., using the data collected fromOrhan the et relevant al. [7] established weather HDDs stations. and CDDs There for 78 aredifferent more cities similar of Turkey studies using the [10 historical–20] where HDDs and CDDs havetemperature been data established from the relevant fordifferent weather stations. parts In ofa similar the world.study, Altan However, [3] established there HDDs are plenty of and CDDs for five different base temperatures for 79 city centres of Turkey using long term historical regions/countriesdata of on 21 the years. globe Al-Hadhrami of which [8] HDDs used temperature and CDDs data have from not 38 been metrological established stations yet and and therefore, forecasting energyestablished consumption HDDs and CDDs of suchfor different regions parts is of always Saudi Arabia a challenging for a base temperature task for of their 18.3 °C. policy makers. This unavailabilityMattia et of al. HDD [4] determined and CDDs HDDs for and different CDDs for Italy climatic for 75 regionsdifferent locati of theons worldfor three has different been considered base temperatures. Viorel and Zamfir [9] established HDDs and CDDs for 29 different cities of as a serious knowledgeRomania using gap the data which collected must from be the filled relevant through weather continuous stations. There research.are more simila Thisr studies study aims to fill this knowledge[10– gap20] where partially HDDs byand focusing CDDs have on been one established country, for i.e., different Pakistan, parts of bythe establishingworld. However, its HDDs and CDDs (at differentthere are base plenty temperatures) of regions/countries of 22 on cities the globe representing of which HDDs different and CDDs climatic have not regions been of Pakistan established yet and therefore, forecasting energy consumption of such regions is always a challenging by using dailytask mean for their historical policy makers. temperature This unavailability data of of HDD last and 30 CDDs years for different (1985 to climatic 2015) regions collected of from the metrologicalthe department world has been of considered Pakistan. as a The serious study knowledge further gap aimswhich must to develop be filled through HDDs continuous and CDDs maps to facilitate the internationalresearch. This study investors, aims to fill policy this knowledge makers gapand partially building by focu designerssing on one to identifycountry, i.e., major heating Pakistan, by establishing its HDDs and CDDs (at different base temperatures) of 22 cities representing and cooling regionsdifferent climatic of Pakistan. regions of Pakistan by using daily mean historical temperature data of last 30 years (1985 to 2015) collected from the metrological department of Pakistan. The study further aims to 2. Backgrounddevelop HDDs and CDDs maps to facilitate the international investors, policy makers and building designers to identify major heating and cooling regions of Pakistan. Pakistan is comprised of four provinces: Punjab, Sindh, Baluchistan and Khyber-Pakhtunkhwa 2. Background (KPK) and four associated territories: the Tribal belt (Federally Administrative Tribal Areas, commonly knownPakistanas FATA), is comprised Gilgit–Baltistan, of four provinces: IslamabadPunjab, Sindh, Baluchistan Capital Territory, and Khyber-Pakhtunkhwa and Azad Jammu and (KPK) and four associated territories: the Tribal belt (Federally Administrative Tribal Areas, Kashmir (AJK).commonly Pakistan known is as bordered FATA), Gilgit by–Baltistan, Afghanistan Islamabad to Capital the west,Territory, India and Azad to the Jammu east, andChina in the northeast andKashmir Iran borders (AJK). Pakistan the country is bordered to by the Afghanistan southwest. to the Pakistan west, India has to the a coastline east, China inof the 1046 km on its northeast and Iran borders the country to the southwest. Pakistan has a coastline of 1046 km on its south. Figure1 shows the map of Pakistan [21]. south. Figure 1 shows the map of Pakistan [21].

Figure 1. Map of Pakistan. Figure 1. Map of Pakistan. Energies 2018, 11, 94 3 of 12

Pakistan covers 796,095 km2 land area, which is approximately equal to the collective terrestrial areas of France and the United Kingdom. In 2015, Pakistan had a population of 19.45 M, and a gross national income per capita of 1275 USD. In recent years, Pakistan has seen some economic growth, i.e., approximately 4.4% over the last 10 years [22]. The climate of Pakistan varies from temperate to tropical having arid conditions in the coastal south. There is a dry season with considerably less or no rainfall and a monsoon season with heavy rainfall followed by frequent flooding. There are four different seasons: a cool, dry winter from December through February; a hot, dry spring from March through May; the summer rainy season, or southwest monsoon period, from June through September; and the retreating monsoon period of October and November [23]. Average temperature varies with highest temperatures recorded in the Southern end near the coastline whereas temperatures in the northern end of Pakistan remain fairly low. Pakistan has recorded one of the highest temperatures in the world, i.e., 53.5 ◦C in its Sindh province on 26 May 2010 [24]. A strong and diverse energy sector making full utilization of available energy resources in a developing country such as Pakistan could play a vital role in the development of its other sectors and could bring socio-economic development to the country [25]. Unfortunately, Pakistan is currently facing a severe energy shortage and has already signed an agreement with Iran to supply natural gas through a 1900-km long pipeline in order to overcome this energy crisis. However, this project has been delayed due to political reasons and the country’s energy short fall is increasing day by day [26]. Oil and gas are the two key components of Pakistan’s current energy mix meeting approximately 62% electricity demand of the country while 34% demand is met by hydro power plants and the remaining 4% by the nuclear power plants [27]. Energy consumption in the building sector has a share of 55% of its annual energy and is the major energy consuming sector of Pakistan [28]. At the time when Pakistan is becoming a key player in Asia after it initiated China Pakistan Economic Corridor (CPEC) project with China and when other countries such as Russia and Iran have shown their desire to join this project, it is expected that Pakistan’s energy sector will have a greater opportunity to contribute towards international carbon reduction targets through greener and sustainable initiatives. Therefore, it is a great opportunity for its policy makers to analyze and forecast energy demands of different regions of Pakistan for attracting more investors from across the world for its energy sector. As discussed earlier, the Degree Day Method could be applied for analyzing and forecasting energy consumption of Pakistan’s different regions. For this purpose, availability of HDD and CDD at different base temperatures is indispensable. At current, a very limited research [29] had been undertaken in this regard which was based on a single year data only and represented only a few cities of Pakistan.

3. Methodology Data for the mean daily temperature for 22 cities located in different provinces of Pakistan for a period of 30 years (i.e., from 1985 to 2015) were obtained from the relevant weather stations and yearly Cooling Degree-Days (CDDs) and Heating Degree-Days (HDDs) were then calculated for each city based on their daily mean temperatures. Table1 presents the Latitude, Longitude, elevation and average annual outdoor temperature of 22 cities in Pakistan. Figure2 shows the relationship between the average temperature of different cities and their corresponding elevation. It is apparent that cities at low elevation such as Karachi and Pasni have high annual mean temperature, i.e., >26 ◦C, whereas cities with high elevation such as Parachinar, Murree and Quetta have lower annual mean temperatures, i.e., <15 ◦C. Linear regression analysis was performed and values for different statistical parameters were found. Parameters such as p-value which is less than 0.05, t-stat value > 1.96 and R2 value of 0.93, demonstrate a strong relationship between elevation and average temperature. Energies 2018, 11, 94 4 of 12

Table 1. Latitude, Longitude and Elevation of different cities in Pakistan.

CityEnergies 2018Province/Area, 11, 94 Latitude (N) Longitude (E) Elevation (m) 30 Years Average Temperature4 of 12 (°C) Karachi Sindh 24.9 67.13 8 26.1 Pasni Baluchistan 25.3 63.5 10 25.5 Badin BaluchistanTable 1. Latitude,24.63 Longitude and68.9 Elevation of different10 cities in Pakistan. 26.7 Hyderabad Sindh 25.38 68.37 13 27.8 ◦ Sukkur CitySindh Province/Area Latitude27.7 (N) Longitude68.85 (E) Elevation67 (m) 30 Years Average Temperature26.9 ( C) Multan KarachiPunjab Sindh 30.2 24.9 71.43 67.13 8122 26.1 25.2 Pasni Baluchistan 25.3 63.5 10 25.5 Sibi BadinBaluchistan Baluchistan 29.54 24.63 67.88 68.9 10130 26.7 27.3 BahawalnagarHyderabad Punjab Sindh 29.33 25.38 73.85 68.37 13163 27.8 25.3 DI Khan SukkurPunjab Sindh 31.82 27.7 70.92 68.85 67165 26.9 24.2 Multan Punjab 30.2 71.43 122 25.2 Lahore SibiPunjab Baluchistan 31.52 29.54 67.8874.4 130217 27.3 24.3 SialkotBahawalnagar PunjabPunjab 32.5 29.33 74.53 73.85 163256 25.3 22.7 PeshawarDI KhanKPK Punjab 34 31.82 70.9271.6 165359 24.2 22.5 Lahore Punjab 31.52 74.4 217 24.3 Islamabad SialkotFederal Punjab 33.43 32.5 73.04 74.53 256540 22.7 21.4 DalbandinPeshawar Baluchistan KPK 28.88 34 64.4 71.6 359843 22.5 20.1 Saidu SharifIslamabad KPK Federal 34.75 33.43 72.35 73.04 540970 21.4 19.3 Dalbandin Baluchistan 28.88 64.4 843 20.1 PanjgurSaidu SharifBaluchistan KPK 26.98 34.75 64.11 72.35 970980 19.3 22.2 Khuzdar PanjgurBaluchistan Baluchistan 27.83 26.98 66.63 64.11 9801237 22.2 21.5 Kakul KhuzdarKPK Baluchistan 34.18 27.83 73.25 66.63 12371300 21.5 15.9 Kakul KPK 34.18 73.25 1300 15.9 Gilgit GilgitGilgit-Gilgit-BaltistanBaltistan 35.9235.92 74.3 15001500 16.8 16.8 Chitral ChitralKPK KPK35.85 35.85 71.83 15171517 15.9 15.9 Quetta QuettaBaluchistan Baluchistan 30.21 30.21 67.02 16801680 15.8 15.8 Parachinar FATA 33.87 70.08 1705 15.0 ParachinarSkardu FATAGilgit-Baltistan 33.8735.29 70.08 75.64 22281705 10.3 15.0 Skardu MureeGilgit-Baltistan Punjab 35.29 33.92 75.64 73.38 22912228 12.7 10.3 Muree Punjab 33.92 73.38 2291 12.7

FigureFigure 2. Variation 2. Variation of mean of mean yearly yearly temperature temperature withwith respect respect to elevationto elevation of different of different cities. cities.

It 4.is Calculation apparent ofthat Degree-Days cities at low elevation such as Karachi and Pasni have high annual mean temperature,Degree-days i.e., >26 ° areC, thewhereas integral cities (summation) with high of the elevation differences such of temperaturesas Parachinar, between Murree a defined and Quetta have lowerbase temperature annual mean and ambienttemperatures, temperature. i.e., <15 Heating °C. Linear degree-day regression is a measure analysis of a value was whenperformed base and valuestemperature for different remains statistical higher para thanmeters the outdoor were temperature.found. Parameters In this way such DDs as for p-value a particular which day is areless than 0.05, t-merelystat value the difference> 1.96 and of R base2 value temperature of 0.93, and demonstrate the daily average a strong ambient relationship temperature. between The base elevation point and averagetemperature temperature. is a principal value to the successful use and understanding of degree-days. Base point temperature is a point, at which the cooling and/or heating system will not need to operate to perform the associated working [30]. 4. Calculation of Degree-Days Degree-days are the integral (summation) of the differences of temperatures between a defined base temperature and ambient temperature. Heating degree-day is a measure of a value when base temperature remains higher than the outdoor temperature. In this way DDs for a particular day are merely the difference of base temperature and the daily average ambient temperature. The base point temperature is a principal value to the successful use and understanding of degree-days. Base point temperature is a point, at which the cooling and/or heating system will not need to operate to perform the associated working [30].

Energies 2018, 11, 94 5 of 12

4.1. Calculation of Heating Degree-Days Real values of heating degree-day numbers for a specific area can be measured using daily average ambient temperatures. Daily, monthly and yearly and yearly average heating degree-day numbers for each city have been calculated using (Equations (1)–(4)) equations:

+ HDDdaily = (Tb − Ta) (1)

m HDDmonthly = ∑ HDDdaily,i (2) i=1 12 HDDyearly = ∑ HDDmonthly,j (3) j=1

30 ∑ = HDDYearly HDD = k 1 (4) yearly average 30 ◦ ◦ In the equations, Ta ( C) is the daily mean ambient temperature, Tb ( C) is base point temperature, and HDD is heating degree-day number. The positive sign in Equation (1) shows that only the positive values of temperature difference (Tb − Ta) should be considered for HDD. If this temperature difference is negative, it should be considered as zero. Annual HDDs for 22 cities for six different base temperatures (i.e., 12 ◦C, 14 ◦C, 16 ◦C, 18 ◦C, 20 ◦C and 22 ◦C) are presented in Table2.

Table 2. Annual average Heating Degree Days (HDDs) of different cities in Pakistan for different base temperatures.

Base Temperature City 12 ◦C 14 ◦C 16 ◦C 18 ◦C 20 ◦C 22 ◦C Pasni 4 16 41 90 171 299 Hyderabad 12 33 74 144 247 391 Karachi 15 38 80 146 241 373 Badin 16 42 86 160 261 402 Sibi 62 116 198 311 462 653 Dalbandin 72 138 231 359 522 727 Sukkur 80 147 246 375 539 742 Bahawalnagar 98 180 296 446 636 866 Multan 114 202 325 483 679 920 DI Khan 134 236 372 546 762 1022 Lahore 139 241 377 551 765 1022 Sialkot 175 297 458 666 917 1209 Khuzdar 214 330 481 679 911 1198 Islamabad 221 364 549 779 1054 1374 Peshawar 227 359 533 751 1015 1325 Panjgur 271 398 556 758 997 1273 Saidu Sharif 442 657 919 1234 1594 2004 Kakul 647 909 1223 1590 2016 2505 Quetta 792 1050 1350 1694 2079 2511 Muree 838 1159 1540 2001 2544 3167 Chitral 877 1193 1551 1952 2397 2880 Parachinar 1376 1761 2189 2656 3163 3710

Figure3 shows the relationship between elevation and the number of HDDs for different cities for a base temperature of 18 ◦C. Linear regression shows that elevation and number of HDDs have a linear positive relationship with p-value < 0.05, t-stat value > 1.96 and R2 = 0.78. It is apparent that HDDs increase with the increase in elevation and are highest for the Parachinar with Murree, Chitral and Quetta and Kakul having the higher number of HDDs after Parachinar. It could be seen Energies 2018, 11, 94 6 of 12

Energiesthat the 2018 capital, 11, 94 city of Pakistan “Islamabad” has a moderate number of HDDs, i.e., 70% less6 than of 12 Parachinar. Cities near the coast line such as Karachi, Sibi and Sukkur have the least number of HDDs, peri.e.,Energies year. <500 2018 perThis, 11 year. means, 94 This a building means a in building Parachinar in Parachinar will have willseven have times seven more times heating more cost heating if the6 of costsame 12 if buildingthe same was building in Islamabad was in Islamabad and approximately and approximately nine times nine more times than more the sam thane thebuilding same buildingin Karachi. in BaseKarachi.per temperature year.Base This temperature means is another a buildingis important another in Parachinar important parameter will parameter whichhave seven can which affect times can themore affectnumber heating the of numbercost HDDs. if the ofFigure same HDDs. 4 showsFigurebuilding 4the shows variation was in the Islamabad variationof HDDs and for of HDDsmajorapproximately cities for major of Pakistannine cities times ofat moredifferent Pakistan than base atthe different samtemperaturese building base temperatures rangingin Karachi. from ◦ ◦ 12ranging Base°C totemperature from 22 °12C. ItC istois another 22apparentC. It important is that apparent the parameternumber that the of numberwhich HDDs can of increaseaffect HDDs the increase with number an with increaseof HDDs. an increase in Figure thein base4 the shows the variation of HDDs for major cities of Pakistan at different base temperatures ranging from temperature.base temperature. This means, This means, the higher the higherthe set thepoint set for point the heating for the heatingplant is, planthigher is, will higher be the will running be the 12 °C to 22 °C. It is apparent that the number of HDDs increase with an increase in the base hoursrunning of hoursthe boiler of the plant boiler and plant higher and will higher be willthe fuel be the consumption. fuel consumption. A building A building with a withset point a set pointof 18 temperature.◦ This means, the higher the set point for the heating plant is, higher will be the running °ofC 18in PeshawarC in Peshawar will have will have72% less 72% heating less heating demand demand than than the same the same building building if it ifwas it was in Parachinar. in Parachinar. hours of the boiler plant and higher will be the fuel consumption. A building with a set point of 18 °C in Peshawar will have 72% less heating demand than the same building if it was in Parachinar.

Figure 3. Variation of yearly HDDs with respect to elevation of different cities. FigureFigure 3. 3Variation. Variation of of yearlyyearly HDDsHDDs with respect to to elevation elevation of of different different cities cities..

FigureFigure 4. 4Number. Number ofof HDDsHDDs forfor different cities at at various various base base temperatures temperatures.. Figure 4. Number of HDDs for different cities at various base temperatures. 4.2. Calculation of Cooling Degree-Days 4.2. Calculation of Cooling Degree-Days Same methodology has been employed in the calculation of CDDs. Real values of cooling degreeSame-day methodology numbers of 22 has different been employed cities are measured in the calculation using daily of average CDDs. ambient Real values temperatures. of cooling degree-day numbers of 22 different cities are measured using daily average ambient temperatures.

Energies 2018, 11, 94 7 of 12

4.2. Calculation of Cooling Degree-Days Same methodology has been employed in the calculation of CDDs. Real values of cooling degree-day numbers of 22 different cities are measured using daily average ambient temperatures. Daily, monthly and yearly and yearly average heating degree-day numbers for each city have been calculated using (Equations (5)–(8)) equations:

+ CDDdaily = (Ta − Tb) (5)

m CDDmonthly = ∑ CDDdaily,i (6) i=1 12 CDDyearly = ∑ CDDmonthly,j (7) j=1

30 ∑ = CDDYearly CDD = k 1 (8) yearly average 30 ◦ ◦ In the equations, Ta ( C) is the daily mean ambient temperature, Tb ( C) is base point temperature, and CDD is cooling degree-days number. The positive sign in Equation (5) shows that only the positive values of temperature difference (Ta − Tb) should be considered. If this temperature difference is negative, it should be considered as zero. Calculated annual CDDs for 22 cities for six different base temperatures (i.e., 18 ◦C, 20 ◦C, 22 ◦C, 24 ◦C, 26 ◦C and 28 ◦C) are presented in Table3.

Table 3. Annual average CDDs of different cities in Pakistan for a base temperature.

Base Temperature City 18 ◦C 20 ◦C 22 ◦C 24 ◦C 26 ◦C 28 ◦C Muree 381 193 89 34 12 3 Parachinar 760 537 353 212 109 46 Kakul 1033 730 490 306 172 87 Chitral 1253 966 720 515 347 214 Quetta 1564 1221 919 667 459 299 Saidu Sharif 1577 1209 886 622 409 246 Islamabad 2251 1794 1385 1020 711 467 Peshawar 2409 1944 1522 1151 829 564 Sialkot 2463 1982 1546 1154 816 548 Khuzdar 2475 1983 1535 1142 802 532 Panjgur 2766 2273 1821 1419 1065 765 Lahore 2840 2326 1852 1418 1033 710 DI Khan 2918 2403 1935 1505 1120 792 Pasni 3052 2247 1797 1251 780 419 Multan 3217 2685 2192 1741 1330 965 Karachi 3249 2615 2015 1461 963 542 Bahawalnagar 3292 2749 2250 1789 1372 1007 Badin 3445 2816 2227 1678 1184 768 Dalbandin 3455 2887 2362 1877 1437 1050 Hyderabad 3539 2911 2324 1785 1295 879 Sukkur 3615 3047 2522 2037 1593 1200 Sibi 3965 3387 2848 2347 1887 1469

From Table3, it is apparent that with the increasing base temperature, the numbers of CDDs drop. This means a building having a set point of 26 ◦C as base temperature for its A/C operation will have less electricity bills compared to a same sized building operating at a lower base temperature as its set point. It is further clear from this table that cities located in the northern regions of Pakistan have a Energies 2018, 11, 94 8 of 12 lower number of CDDs whereas cities located in the South closer to the coast line of the Arabian sea such as Badin, Sukkur, Sibi, Karachi and Hyderabad have the highest number of CDDs. It is evident thatEnergies a building2018, 11, 94 located at Sibi will have 8 times higher cooling energy bills if the same building8 wasof 12 inEnergies Parachinar. 2018, 11, 94 8 of 12 Figure 55 shows shows thethe variationvariation ofof yearlyyearly CDDsCDDs withwith respectrespect toto thethe elevation elevation ofof cities. cities. AA strongstrong Figure 5 shows the variation of yearly CDDs with respect to 2the elevation of cities. A strong negative linear linear relationship relationship (p (p-value-value = =0.0002, 0.0002, t-stat t-stat = − =4.99−4.99 and andR2 = 0.76) R = is 0.76) obvious is obvious between between the CDDs the negative linear relationship (p-value = 0.0002, t-stat = −4.99 and R2 = 0.76) is obvious between the CDDs CDDsand the and elevation the elevation suggesting suggesting that with that the with increasing the increasing elevation, elevation, CDDs CDDs decrease. decrease. Cities Cities which which are and the elevation suggesting that with the increasing elevation, CDDs decrease. Cities which are arelocated located at high at high elevations elevations such suchas Murree, as Murree, Parachinar, Parachinar, and Quetta and Quetta have the have lowest the lowest number number of CDDs of located at high elevations such as Murree, Parachinar, and Quetta have the lowest number of CDDs CDDscompared compared to the cities to the which cities are which located are locatedat low elevation at low elevation such as Sukkur, such as Sibi, Sukkur, and Sibi,Karachi. and Figure Karachi. 6 compared to the cities which are located at low elevation such as Sukkur, Sibi, and Karachi. Figure 6 Figureshows6 the shows variation the variation of CDDs of for CDDs major for cities major of cities Pakistan of Pakistan at different at different base temperatures base temperatures ranging ranging from shows the◦ variation◦ of CDDs for major cities of Pakistan at different base temperatures ranging from from18 °C18 to C 28to °C. 28 ItC. is It apparent is apparent that that the the number number of of CDDs CDDs decrease decrease with with an an increase increase in in the the base 18 °C to 28 °C. It is apparent that the number of CDDs decrease with an increase in the base temperature. This This means, means, the the lower lower the the set set point for cooling plant is, the higher will be its running temperature. This means, the lower the set point for cooling plant is, the higher will be its running hours and the higher will be the electricity bill. hours and the higher will be the electricity bill.

Figure 5. Variations of yearly CDDs with respect to elevations of different cities. Figure 5. Variations of yearly CDDs with respect to elevations of different cities.cities.

Figure 6. Number of CDDs for different cities at various base temperatures. Figure 6. Number of CDDs for different cities at various base temperatures.temperatures. 5. Results and Discussion 5. Results and Discussion Annual mean HDDs and CDDs of 22 cities in Pakistan located in the different regions have been Annual mean HDDs and CDDs of 22 cities in Pakistan located in the different regions have been calculated using Equations (1) to (8) by using the daily mean temperature data of each city obtained calculated using Equations (1) to (8) by using the daily mean temperature data of each city obtained from the Metrological office of Pakistan as listed in Table 1. Annual HDDs of 22 cities have been from the Metrological office of Pakistan as listed in Table 1. Annual HDDs of 22 cities have been calculated for six different base temperatures, i.e., 12 °C, 14 °C, 16 °C, 18 °C, 20 °C and 22 °C. calculated for six different base temperatures, i.e., 12 °C, 14 °C, 16 °C, 18 °C, 20 °C and 22 °C. Measured values indicated that the elevation of the cities have a significant effect on the number of Measured values indicated that the elevation of the cities have a significant effect on the number of HDDs. Cities at higher elevation and higher latitudes have higher numbers of HDDs and vice versa, HDDs. Cities at higher elevation and higher latitudes have higher numbers of HDDs and vice versa,

Energies 2018, 11, 94 9 of 12

5. Results and Discussion Annual mean HDDs and CDDs of 22 cities in Pakistan located in the different regions have been calculated using Equations (1) to (8) by using the daily mean temperature data of each city obtained from the Metrological office of Pakistan as listed in Table1. Annual HDDs of 22 cities have been calculated for six different base temperatures, i.e., 12 ◦C, 14 ◦C, 16 ◦C, 18 ◦C, 20 ◦C and 22 ◦C. Measured values indicated that the elevation of the cities have a significant effect on the number of HDDs. Cities at higher elevation and higher latitudes have higher numbers of HDDs and vice versa, e.g., the highest numbers of HDDs at a base temperature of 18 ◦C were observed in Parachinar (2656), Murree (2001), Chitral (1952) and Kakul (1590) whereas cities on the southern end near the coastline experience lowest HDDs, e.g., Pasni (90) and Karachi (146). This means that heating energy consumption (natural gas consumption) will be 29 times more in Parachinar compared to Pasni for a same building operating under the same conditions. HDDs values in the cities of the northern and central regions of Pakistan are higher than the cities located on the southern end near the coastline. This clearly suggests that cities at higher altitudes have a higher number of HDDs and thus higher heating energy demands and vice versa. Figure7 shows the HDDs map of Pakistan. This HDDs map of Pakistan should be helpful for policy makers to identify the major heating regions and international investors as it could help in making decisions for bringing gas pipelines from the neighboring countries, e.g., this HDDs map of Pakistan is suggesting that Pakistan’s northern region has higher heating demands. As Pakistan is currently negotiating with Iran for a gas pipe line, it may also look to Tajikistan for gas supply through Afghanistan as Tajikistan is relatively closer to these regions than Iran. This will certainly have a significant impact on the capital and operational costs of the project.

FigureFigure 7. 7. HDDsHDDs map ofofPakistan. Pakistan Energies 2018, 11, 94 10 of 12

Using the same methodology as for HDDs, CDDs were calculated for the same 22 cities for six different base temperatures, 18 ◦C, 20 ◦C, 22 ◦C, 24 ◦C, 26 ◦C and 28 ◦C. It was observed that cities located in the central and northern end of Pakistan experience lower CDDs whereas cities located on the southern end have a higher number of CDDs. For example, Parachinar has 760 CDDs compared to Sukkur which has the highest number of CDDs among these 22 cities, i.e., 3615 at a same base temperature of 18 ◦C. This means the cooling energy consumption in Sukkur will be nine times higher in a same building located in Parachinar. CDDs are lower for the cities located at higher altitudes such as Parachinar, Quetta and Murree and vice versa. At higher base temperatures, the CDDs drop linearly showing lower energy demand for cooling purpose. Figure8 shows a CDDs map of Pakistan which has been developed based on the calculated CDDs at a base temperature of 18 ◦C. Energies 2018, 11, 94 10 of 12

FigureFigure 8. CDDCDD map map of of Pakistan Pakistan..

Variation in HDDs and CDDs at different base temperature suggests that the set point for the heating/coolingVariation in HDDs plant should and CDDs be selected at different very carefullybase temperature in order to suggests optimize that the the operation set point of forthe the heating/coolingHVAC plant. Usually, plant should by applying be selected energy very efficiency carefully measures in order in to the optimize building the such operation as better of the HVACinsulation, plant. optimum Usually,by values applying of base energy temperature efficiency could measures be set. in This the buildingway the operation such as better and insulation,cost of optimumHVAC could values be of optimized base temperature and controlled. could After be set. analyzing This way the the data, operation a set point and of cost20 °C of for HVAC heating could ◦ beseason optimized and 2 and4 °C controlled. for cooling Afterseason analyzing is recommended the data, for a Pakistan. set point These of 20 setC points for heating are within season the and 24range◦C for that cooling is acceptable season isto recommended80 percent of the for building Pakistan. occupants These setper pointsASHRAE are Standard within the 55, range Thermal that is acceptableEnvironmental to 80 percent Conditions of the for building Human Occupancy occupants per[31]. ASHRAE Standard 55, Thermal Environmental Conditions for Human Occupancy [31]. 6. Conclusions 6. Conclusions Buildings in Pakistan consume about 55% of Pakistan’s annual energy of which a higher proportionBuildings is consumed in Pakistan for consumemeeting the about heating 55% and of cooling Pakistan’s demands. annual Degree energy days of (DD) which represent a higher proportiona versatile is climatic consumed indicator for meeting which is the commonly heating andused coolingin the analysis demands. of building Degree energy days (DD) performance represent a and helps policy makers in analyzing and forecasting energy consumption of different climatic regions. In this study, annual HDDs and CDDs of 22 different cities representing different regions of Pakistan have been established at various base temperatures using the most updated daily mean temperature data for a period of 30 years (i.e., 1985–2015) collected from the metrological department of Pakistan. Cities located at higher altitude such as Quetta, Murree and Parachinar have higher HDDs and lower CDDs whereas cities located in the central and southern end of Pakistan such as Islamabad, Sukkur, and Karachi experience lower HDDs and higher CDDs. Selecting an optimum base temperature for the heating and cooling plants is crucial in order to optimize the performance of HVAC plants and to control HVAC related costs in buildings. Using ASHRAE standards and the collected data, set points of 20 °C and 24 °C are recommended for winter season and summer seasons respectively for Pakistan’s building sector. It is anticipated that this study would directly help the policy makers in the development of energy consumption policies for the buildings sector. Further,

Energies 2018, 11, 94 11 of 12 versatile climatic indicator which is commonly used in the analysis of building energy performance and helps policy makers in analyzing and forecasting energy consumption of different climatic regions. In this study, annual HDDs and CDDs of 22 different cities representing different regions of Pakistan have been established at various base temperatures using the most updated daily mean temperature data for a period of 30 years (i.e., 1985–2015) collected from the metrological department of Pakistan. Cities located at higher altitude such as Quetta, Murree and Parachinar have higher HDDs and lower CDDs whereas cities located in the central and southern end of Pakistan such as Islamabad, Sukkur, and Karachi experience lower HDDs and higher CDDs. Selecting an optimum base temperature for the heating and cooling plants is crucial in order to optimize the performance of HVAC plants and to control HVAC related costs in buildings. Using ASHRAE standards and the collected data, set points of 20 ◦C and 24 ◦C are recommended for winter season and summer seasons respectively for Pakistan’s building sector. It is anticipated that this study would directly help the policy makers in the development of energy consumption policies for the buildings sector. Further, this study is valuable for the international investors, building designers, building owners and building managers as it provides a foundation for analyzing and forecasting the performance of buildings based on the data presented in this study.

Acknowledgments: The authors are thankful to the Pakistan Metrological Department (Pak Met) for providing necessary data for the completion of this study. Author Contributions: Khuram Pervez Amber and Muhammad Waqar Aslam conceived the idea and performed the analysis. Faraz Ikram and Anila Kousar contributed in the write up of the manuscript and also gave useful insights during the data analysis. Hafiz Muhammad Ali and Naveed Akram prepared the relevant graphics and tables and contributed in the writing of description of these graphics and tables. Kamran Afzal and Haroon Mushtaq contributed during data collection and gave useful comments for the improvement of the paper. Conflicts of Interest: The authors declare no conflict of interest.

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