Improving Sri Lankan Buildings' Energy Efficiency Through Bioclimatic Classification and Potential Assessment

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Introduction
Buildings are the one of the largest end users of generated energy (40%) a significant contributor of CO2 which is a driving factor of global warming [1,2] . There is an ever-increasing demand for thermal comfort in the built environment. Outdoor climatic factors play a vital role in building's cooling and heating demand. Therefore, it is crucial to introduce bioclimatic concepts into building design approaches to minimize energy requirements and CO2 emissions. To design energy efficient buildings designers must take the local climate into account [3]. Bioclimatic design strategies help to reduce dependency of mechanical heating cooling, hours of artificial lighting. The determining factors that decrease the building's energy consumption are its orientation, window technology, and microclimate [4] Sri Lanka's energy needs are met through imported fuel resources. Furthermore, it is imperative for Sri Lanka to implement policies and laws related to energy efficiency. Sri Lanka's energy needs are met through imported fuel resources. Sri Lanka is an island country with a population of 21 million people and over 5 million housing units [5]. The industrial and commercial sectors have a significant number of high-rise structures, which consume 60% of the total power generated in the country for lighting and space cooling [6]; therefore, implementation of energy efficiency policies and laws is imperative for Sri Lanka. Many attempts were made to develop and implement a building code for Sri Lanka, * N Pravin Diliban: pravindiliban@gmail.com but implementation failed due to differing views among stakeholders [6,7]

Climate Classification
The indoor thermal environment of the built environment is directly affected by climatic factors such as ambient temperature, humidity, wind speed and direction, and sky conditions. Ignoring the climate aspect throughout the design process can increase energy use and discomfort within the building [8] Bioclimatic zoning accommodates climate variability of location, therefore, helps building designers to build climate-conscious buildings [9,10] . Köppen-Geiger classifications are made with respect to agricultural, vegetation and climatic aspect. However, Sri Lanka is not classified its climatic in the context of building energy. In present research an extensive effort has been put to develop bioclimatic zones and appropriate passive design strategies for Sri Lanka.

Objective of the study
A crucial step in creating building energy efficiency guidelines is bioclimatic zoning of the region/country. This study was carried out to define bioclimatic zones of Sri Lanka based using degree days method based on 30 years hourly climatic parameters data. In this study range of climatic parameters were also defined respective to developed zone. This is a comprehensive study to lay the groundwork for developing bioclimatic zoning for the entire island of Sri Lanka.

Temperature Data
From 1989 to 2019, the hourly maximum and minimum temperatures at all 25 study locations as shown in figure  2 were accessed from a meteorological website [12]. The meteorological website collects data from various sources such as the network of weather stations, along with data from global/local data providers. The meteorological website provides reliable climatic data for any given coordinate an accuracy of 99 % [13].

Irradiance Data
For all study locations, global irradiance data was collected from European Commission's Joint Research Centre for the duration of 2006-2016 at a slope of 0ᴼ and azimuth angle of 0ᴼ [14]. As seen in Fig 3, the irradiance does not significantly vary across the island. However, areas like Kandy, Badulla, and Nuwara Eliya exhibit relatively lower solar irradiance. And in terms of the monthly trend, solar irradiance peaks in March at a mean of 487 W/m 2 and gradually declines to reach its trough in July at 427 W/m 2 . Again, in October, a secondary peak with a mean of 435 W/m 2 , is also observed.

Degree Days Estimation Method
Climatological parameters such as outdoor temperature, humidity and windspeed and directions determines the energy demand in a building to maintain adequate thermal comfort [15,16] . A degree-day (DD) is a clear method of estimating energy load [17]. In Heating Degree Day (HDD) the outdoor temperature (To) is below the set point temperature, while in Cooling Degree Days (CDD) the outdoor temperature (To) above the setpoint temperature. Degree Days are the timeline which shows required period of cooling or heating demand of a building, which helps to determine the fuel budget, developing energy policies of a country [18].

Hourly Method
This is the most theoretically accurate to estimate the DD for a given location [19]. The daily average of the positive difference between hourly outdoor temperature (To) and reference or setpoint temperature (Tset) provides the DD value. The total degree hours of a day are divided by 24, and the average degree is called the degree-days. The daily heating degree-days (HDDd) and daily cooling degree-days (CDDd) can be estimated by using the following equation 1 and 2.

ASHRAE Method
The proposed method uses the difference between the average outdoor temperature and the set point temperature to estimate the CDD or HDD of a specific location [2,19,20]. The method of estimating degree days is denoted in Equations (3) and (4).
Where Tmd is calculated from daily maximum and minimum temperatures, Tmax, Tmin, respectively.

UK Met Office Method
The UK Met Office method has been in practice since 1928, unlike ASHRAE method, UKMO method uses diurnal variation, which improves the estimation of degree days [15,16,19]. As shown in Equations 8 and 9, the daily heating and cooling degree days can be estimated by different conditions.

Schoenau and Kehring's (S-K) Method
The S-K method estimates monthly degree day values by considering a normal distribution of daily mean temperature around the monthly mean temperature (Tmm) in contrast to other methods [9] The heating and cooling degree day estimation equations are given by Equations 7 and 8.
Where, Nm number of days in the month, Sd is the standard deviation of monthly mean temperature (Tmm) where the function f (Z) is Gaussian probability density function with mean=0 and SD=1, and function F(Z) is equivalent cumulative normal probability function is estimated by the following equations.
The monthly cooling degree days, CDDm is given in

Hitchin's Method
Many attempts are made to estimate degree day values for reduced data, such as Thom and Erb's for American climatic conditions [15]. Furthermore, these techniques are based on the monthly average temperature and standard deviation values. Similar to that, Hitchin [16,19] suggested a method to calculate the monthly degreeday values for the UK climate. For a given month, equations 16 and 17 give the monthly DD values.
Nm-the number of days in a month. Tmm-the monthly mean temperature. Tb-the base temperature. k is a location-specific constant given by Equation (15). k has to be estimated by using equation 18 if daily temperature and its mean monthly standard deviation are known. If not, predetermined values for various UK locations can be used to avoid the error [19].

Erb's Method
Erb formulated an efficient method for predicting monthly degree days based on mean monthly temperatures (Tmm), standard deviations of mean monthly temperatures from the annual mean (σyr) and mean daily temperatures from the monthly mean(σm), to compute DD. Equations 16 and 17 give the standard deviation of mean monthly temperature mean yearly temperature, respectively.
The degree days are estimated as expressed through equation 18 and h for heating and cooling are estimated by using equations 19 and 20, respectively.
Where Nm is the number of days in a month, Tmy is the yearly mean temperature, and a=1.698 √Nm.

Environmental profile
The landscape of Sri Lanka influences temperature variation; the difference is minimal up to an elevation of 150 m, but the temperature drop is exponential in central hill stations. The coastal district of Trincomalee has been recorded with the highest annual temperature of 29 °C, at an elevation of 9 m. The central hill district of Nuwara Eliya has been recorded as having the lowest temperature of 22°C at an elevation of 1898 m in Sri Lanka. Due to the rise in solar irradiance, the temperature at all 25 locations is high from March to September. Due to the combined effect of high humidity due to the monsoon and cloudy conditions, temperatures were recorded higher in June and July at all the study locations. The island is dominated by the south-west and west winds. The highest annual average wind speed range in the country is 6-7 m/s in Jaffna, Kilinochchi, Mannar, and Hambantota, while the lowest annual wind velocity range is 1-4 m/s in the North Central, Eastern, South-eastern, and Central parts of the country. Ratnapuara has the lowest annual average wind speed of less than 1 m/s.

Heating degree days
The monthly HDD is estimated by the hourly method at selected locations. While the temperature decreases continuously with altitude, the HDD values increase in lockstep with the temperature decrease. The heating load is prominent in the highlands (Nuwara Eliya, Kandy, Badulla, Matale, Monaragala, and Ratnapura). However, it is insignificant in the other parts of the island. The HDD is highest between December and February, i.e., during the winter months, and lowest between April and September, or during the summer months. Furthermore, the annual average HDD has been estimated for setpoint temperatures of 14°C, 16°C, 18°C, and 20°C using ASHRAE, Erb's, Hitchin's, Schoenau, and Kehring's UKMO methods. The comparison of HDD values estimated using all five other methods with the hourly methods for various base temperatures has been conducted. The hourly annual average HDD values for Nuwara Eliya are presented for comparison. The variation between the annual average HDD values between the hourly method and other methods is plotted against the variation between the yearly mean temperature (Tmy) and the setpoint temperature (Tset) (Fig 5). It can be seen from Fig. 5 that when the difference between Tmy and Tset is high, all models predict a large variation. However, when the difference is small, all the models predict comparable results.

Fig 5 Difference in trends for annual HDD comparing with
Hourly method for Nuwara Eliya

Cooling degree days
Due to the least variation in temperature with elevation, the coastal region and plains of Sri Lanka have a higher value of CDD. The CDD is higher in the summer months (April-September), reaching a peak in June and July. It has been noted that the CDD value drops with the decrease in setpoint temperature. The hourly method is used to estimate CDD, just like HDD estimation. Due to the tropical nature of Sri Lanka, the occupants could withstand higher temperatures and be comfortable, but they feel discomfort even at 24 °C. neutral temperature is 26 o C at the low altitude of Sri Lanka [21]. Furthermore, the yearly average CDD has been estimated for setpoint temperatures of 22°C, 24°C, 26°C, and 28°C using ASHRAE, Erb's, Hitchin's, Schoenau, and Kehring's UKMO methods.

Fig 6 Difference in trends for annual CDD comparing with Hourly method for Nuwara Eliya
The comparison of CDD values has been calculated using all these methods and hourly methods for various base temperatures. The hourly annual average CDD values for Nuwara Eliya have been used as a comparison example and are shown in Fig 6. It could be concluded from Fig. 6 that CDD values between different methods remain close when the temperature difference is greater; in contrast, the CDD values drastically change when the temperature variation is more diminutive. As mentioned above, the hourly method is the most accurate method due to its evaluation on an hourly scale, which would help to analyse the day and night comfort calculations. On the other hand, other methods use reduced data. Therefore, the choice of a particular method depends on the availability of the data.

.1 Using environmental parameters
To identify the districts which can be clubbed/clustered together, the long-term climatic parameters, as well as derived parameters, are plotted over a year. For example, when the monthly average temperature of various locations is plotted (Fig. 4), three distinct temperature ranges appear. The districts that lie in these three bands can be clubbed together to conduct further analysis. The monthly average temperature of all the locations is analyzed, and it is revealed that Badulla, Kandy, Matale, Nuwara Eliya, and Ratnapura have shown a year-round average temperature range of 22°C-24°C throughout the year. The average monthly temperature trend remained consistent throughout the year, and it has been designated as temperature zone 1 (T1). Colombo, Galle, Gampaha, Hambantota, Kalutara, Kegalle, Kurunegala, Matara, and Monaragala show similarities in the monthly average temperature trends (24°C-26°C), and these areas are clubbed as temperature zone 2 (T2). The locations that show a monthly average temperature range of more than 26 °C are grouped as temperature zone 3 (T3). The monthly temperature trend for all locations: analysis shows that the average temperature is significantly higher during March-September for all the locations and low in the winter months, i.e., October-February (Fig 7). This classification enables us to analyse and arrive at a result applicable to all places in the zone's building design study.  According to the data in (Fig 8), the heat demand for hot and intermediate zones is low, but for cold zones it peaks in winter. Therefore, heating is needed in the cold zone during the winter months to restore comfort.

Using degree days method
On the other hand, Fig 9 depicts the CDD trend, which indicates a greater cooling need for all 3 zones throughout the summer. Analyses of the environmental and DD zones both revealed a consistent trend. While T1, T2, and T3 are comparable to CDD1, CDD2, and CDD3 during the summer, HDD1, HDD2, and HDD3 are like them throughout the winter.
For Sri Lanka, bioclimatic zones are created taking these patterns into account. As the cold zone is represented by T1, CDD1, and HDD1, the intermediate zone is represented by T2, CDD2, and HDD2, and the hot zone is represented by T3, CDD3, and HDD3, the temperature, cooling degree day, and heating degree day zones (Fig 10). The districts of Sri Lanka listed in Table  1 correspond to different bioclimatic zones.

Conclusions
This study develops the bioclimatic zones for Sri Lanka for the first time and also estimates the degree days for all the bioclimatic zones of Sri Lanka. The solar passive design strategies for these bioclimatic zones are identified using BcChart v2.0. The annual cooling degree days decrease as the setpoint temperature rises above 20 degrees Celsius, while heating degree days rise as the base temperature rises. The annual HDD values for all three zones are lower than the annual CDD values, which implies that energy consumption for the cooling load will be much higher than the heating load for this country due to its geographical location. Furthermore, a rise in monthly degree day values has been observed for both HDD and CDD, from April to September for CDD and October to February for HDD, implying that seasonal passive techniques have been integrated into the building design for an effective climate-conscious building. It is found that, using solar passive techniques, comfort can be achieved by natural ventilation for the cold zone and intermediate zone during the warmer season of the year. However, the hot zone requires additional cooling by mechanical means, and shading could be utilised to achieve comfort, which reduces the mean radiant temperature. Table 1 Districts under the different bioclimatic zones in Sri Lanka Table 2 Specification of classified bioclimatic Zones of Sri Lanka The cold zone requires a significant portion for passive heating during the cold season of the year, which can be achieved by solar irradiance. The right selection of setpoint temperature is important to optimise the energy load in the building for both cooling and heating load. However, a detailed thermal comfort survey is required in all bioclimatic zones to derive the comfort temperature for both the summer and winter seasons. These passive design techniques can be considered during the initial design stage of a building. The design techniques may be related to the building orientation, building envelope, thermal properties of building materials, fenestration, window-to-wall ratio, shading, and ventilation. It helps building designers and planners to design climate-conscious and energy-efficient buildings on the island. support of US$240 provided by Tezpur University for the purchase of the data.