The Estimation of Base Temperature for Heating and Cooling Degree-Days for South Korea

Kyoungmi Lee National Institute of Meteorological Research, Korea Meteorological Administration, Seoul, South Korea

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Hee-Jeong Baek National Institute of Meteorological Research, Korea Meteorological Administration, Seoul, South Korea

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ChunHo Cho National Institute of Meteorological Research, Korea Meteorological Administration, Seoul, South Korea

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Abstract

In South Korea, heating degree-days (HDD) and cooling degree-days (CDD) have been widely used as climatic indicators for the assessment of the impact of climate change, but arbitrary or customary base temperatures have been used for calculation of HDD and CDD. The purpose of this study is to determine real base temperatures to accurately calculate HDD and CDD for South Korea, using monthly electric energy consumption and mean temperature data from 2001 to 2010. The results reveal that the regional electricity demand generally depends on air temperature in a V-shaped curve in urban settings but in an L-shaped curve in rural settings, indicating that the sensitivity of the electricity demand to the temperature change is affected by the size of cities. The South Korean regional base temperatures, defined by a piecewise linear regression method, range from 14.7° to 19.4°C. These results suggest that the assessment of climate change impacts on the energy sector in South Korea should be carried out on a regional scale.

Denotes Open Access content.

Corresponding author address: Kyoungmi Lee, National Institute of Meteorological Research, Korea Meteorological Administration, 45 Gisangcheong-gil, Dongjak-gu, Seoul 156-720, South Korea. E-mail: leekm80@korea.kr

Abstract

In South Korea, heating degree-days (HDD) and cooling degree-days (CDD) have been widely used as climatic indicators for the assessment of the impact of climate change, but arbitrary or customary base temperatures have been used for calculation of HDD and CDD. The purpose of this study is to determine real base temperatures to accurately calculate HDD and CDD for South Korea, using monthly electric energy consumption and mean temperature data from 2001 to 2010. The results reveal that the regional electricity demand generally depends on air temperature in a V-shaped curve in urban settings but in an L-shaped curve in rural settings, indicating that the sensitivity of the electricity demand to the temperature change is affected by the size of cities. The South Korean regional base temperatures, defined by a piecewise linear regression method, range from 14.7° to 19.4°C. These results suggest that the assessment of climate change impacts on the energy sector in South Korea should be carried out on a regional scale.

Denotes Open Access content.

Corresponding author address: Kyoungmi Lee, National Institute of Meteorological Research, Korea Meteorological Administration, 45 Gisangcheong-gil, Dongjak-gu, Seoul 156-720, South Korea. E-mail: leekm80@korea.kr

1. Introduction

The global average surface temperature has increased by 0.74° ± 0.18°C over the past hundred years (1906–2005), and the warming has impacted human activities and ecosystems (Solomon et al. 2007). Among the various economic consequences of a global temperature rise, the impact on energy consumption is of particular importance and may well represent a large part of the total economic impact of climate change (Tol 2009). Furthermore, greenhouse gases emitted by the energy sector are themselves a main driver of climate change (Solomon et al. 2007). Energy consumption thus affects and is affected by climate change, and therefore information about energy consumption leads to an improved understanding of the possible impacts of changing climate, as well as providing further evidence for potential adaptation strategies.

Heating and cooling consumption depends, in part, on external temperatures. Heating degree-days (HDD) and cooling degree-days (CDD) provide a powerful yet simple way of analyzing weather-related energy consumption as a measure of the severity and duration of hot or cold weather. Thus, these are routinely used by many researchers to estimate the impact of climate change on the energy sector (Matzarakis and Balafoutis 2004; Christenson et al. 2006; CIBSE 2006). Degree-days are essentially a summation of the differences between a reference temperature and the outdoor air temperature over a period of time. The reference temperature is known as base temperature Tb and is defined as the outdoor temperature at which the heating (or cooling) systems in a building do not need to run to maintain comfort conditions (ASHRAE 2001). When the outdoor temperature is below (above) the base temperature, the heating (cooling) systems are required to operate, and therefore deviations result in increased energy requirements.

One of the problems of applying HDD and CDD is the accuracy of a definition of base temperature, which contains both climate and building information itself and relates to the energy consumption (Krese et al. 2012). The base temperature is generally determined by the energy signature method or the performance line method. An energy signature is a plot of electric energy consumption against mean temperature, and the intercept of weather-independent and weather-dependent electricity demand represents the base temperature. On the other hand, performance lines are essentially best-fit straight lines through data on scatterplots of electric energy consumption against HDD or CDD, and the base temperature is determined by putting a best-fit second-order polynomial through a scatterplot of HDD or CDD versus electricity consumption, and varying the base until the polynomial best approaches linearity. Traditionally used base temperatures to calculate HDD and CDD are 18.3°C in the United States, 15.5°C in the United Kingdom, and 15.0°C in Germany (Carbon Trust 2007; Moustris et al. 2011). HDD and CDD are also generally calculated at variable and arbitrary base temperatures or using a base temperature of 18°C as a comfort temperature (Büyükalaca et al. 2001; Priya et al. 2011).

Many researchers in South Korea have used HDD and CDD for analyzing regional climatic characteristics, and for predicting energy demand (Lee 1980; Choi 2005; Kim and Suh 2006; Cho et al. 2010). To establish devices for heating buildings and to employ a policy for fuel supply and consumption, Lee (1980) calculated regional HDD at a base temperature of 18°C, which was encouraged as a reference temperature for heating by the South Korean government. Choi (2005) examined the characteristics of thermal climate over South Korea using HDD and CDD, which are calculated at base temperatures of 18°C from Lee (1980) and 26°C as a comfortable temperature, respectively. To estimate the heating and cooling energy demand in buildings based on climate change scenarios with seven global climate models, Kim and Suh (2006) used base temperature values of 15° and 18°C, referencing the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) for HDD and CDD calculations. Cho et al. (2010) chose five base temperatures, ranging from 24° to 16°C, in an attempt to determine HDD for 15 main cities in South Korea. In these previous studies on HDD and CDD for South Korea, the base temperatures were determined by reference to threshold values used in the United States and/or arbitrary threshold values, neither of which reflect national or regional South Korean energy consumption.

The energy sector is expected to be one of the sectors most at risk from climate change, along with the agriculture, tourism, water distribution, and health sectors, and HDD and CDD are important climatic indicators to estimate energy consumption due to space heating and cooling. It is therefore necessary to define the true base temperature for South Korean HDD and CDD calculation to accurately estimate the impact of climate change on future electricity demand and to establish very real energy policies, decisions, and institutions in South Korea. In this paper, the real base temperature for South Korea is defined through the quantification of the historical monthly residential and commercial electric energy consumption, and correlation with monthly-mean temperatures.

2. Data and methods

Monthly electric energy consumption data for the period 2001–10 were obtained from Korea Electric Power Corporation and daily-mean temperature Td data from 1971 to 2012 were provided by the Korea Meteorological Administration. Monthly-mean temperatures were calculated using Td data. The 35 regions without missing data during the period were selected to study the relationship between electricity consumption and mean temperature. Figure 1 shows a map of station locations, and Table 1 lists the geographical characteristics, including longitude, latitude, and height; population totals in 2005; and mean annual temperature. Stations were classified as either urban (population of more than 100 000) or rural (population of less than 100 000). In addition, urban stations were also subdivided into large urban (population > 1 000 000) and small urban (population < 1 000 000).

Fig. 1.
Fig. 1.

Locations of 35 observational stations in South Korea with their urban–rural area breakdown. The stations with their names and numbers are listed in Table 1.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

Table 1.

Geographical characteristics, population statistics in 2005, and mean annual temperature T during the period 2001–10 for the 35 stations, with their numbers.

Table 1.

The electric energy consumption in South Korea is divided into four broad sectors: industrial, commercial, residential, and public. The industrial sector is the largest electric energy user, representing about 50% of the total. Next in importance is the commercial sector (about 30%), followed by the residential (about 15%) and public (about 5%) sectors. Of these, the sum of residential and commercial electricity consumption was used to estimate base temperature through the analysis of the relationship with temperatures. Industrial electric energy consumption is not used, since previous investigations as well as this study’s findings indicate that it is not temperature sensitive (Elkhafif 1996; Sailor and Munoz 1997; Amato et al. 2005). Additionally, previous studies have shown that air temperature is the most significant weather variable affecting electricity consumption, while other variables (wind speed, humidity, etc.) may be correcting terms for the influence of temperature (Yan 1998; Valor et al. 2001).

The best-known approach to link energy demand with outdoor temperature is through the concept of degree-days. HDD and CDD describe the deviation of daily-mean temperature from a base temperature. The degree-days are intended to correspond to the requirement for heating or cooling at the given temperature in different locations. The monthly and/or annual HDD and CDD, therefore, form indicators for cold and heat stress, respectively, as well as forming a relatively simple description of a region’s climate. HDD and CDD are calculated as follows:
eq1
eq2
where N is the number of heating or cooling days of a certain time period, and the plus symbol means that only positive differences between Tb and Td are taken into account.

The degree-day methodology assumes a V-shaped temperature–energy consumption relationship, as shown in Fig. 2 (Jager 1983; Amato et al. 2005; Hekkenberg et al. 2009). As outdoor temperatures deviate above or below the base temperature, energy demand increases proportionally. In this study, the observed relationship between electricity consumption and temperature was simplified by two linear functions of temperature in the V-shaped plot, using a piecewise linear regression method. Also simple linear detrending was used to attempt to remove any possible anthropogenic signal in the observed residential and commercial electricity consumption data (Karoly et al. 2003).

Fig. 2.
Fig. 2.

Theoretical relationship between temperature and energy use.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

A piecewise linear regression can be used to simplify a problem by replacing a complex model with smaller sections of it, as components (McGee and Willard 1970; Ryan and Porth 2007). This gives a set of slopes, a set of breakpoints at which the slopes change, and the value of the functions at a given point. When analyzing a relationship between a response y and a varying influential factor x, different linear relationships may occur for different ranges of x. In this case, a single linear model may not provide an adequate description and a nonlinear model may not be appropriate either. Breakpoints are the values of x where the slope of the linear function changes and can be interpreted as a threshold value beyond or below which effects occur. The least squares method is applied separately to each segment, by which the two regression lines are made to fit the dataset as closely as possible while minimizing the sum of squares of the differences between observed and calculated values of the dependent variable. When there is only one breakpoint, at x = c, the model can be written as follows:
eq3
eq4
where and are regression coefficients indicating the slope of the line segments, and and are regression constants indicating the intercept at the y axis. In order for the regression function to be continuous at the breakpoint, the two equations for y need to be equal at the breakpoint (when x = c):
eq5
eq6
Then by replacing with the equation above, the result is a piecewise regression model that is continuous at x = c:
eq7
eq8
A piecewise nonlinear regression tool in the Sigmaplot software package for data analysis and scientific graphing was used to fit this model to the data.

3. Results

a. Monthly electricity consumption and temperature

The electricity consumption for a nation/region is influenced by various factors, such as the extent of electrification, the population density, the general degree of economic welfare, the availability of other energy carriers, the prevalence of energy efficient technologies, the prevailing climate, and cultural habits (Henley and Peirson 1997; Amato et al. 2005; Hadley et al. 2006). Figure 3 shows the evolution of both the monthly electricity demand and monthly-mean temperature in Seoul (large urban) and Sancheong (rural) from 2001 to 2010. The overall upward trends for electricity consumption in both Seoul and Sancheong are observed, while the temperatures demonstrate intra-annual oscillation but no interannual trend. This indicates that the electricity consumption is linked to various socioeconomic factors, as well as to temperature.

Fig. 3.
Fig. 3.

(left) Monthly electricity consumption (MW h) and (right) monthly-mean temperature (°C) in (top) Seoul and (bottom) Sancheong from January 2001 to December 2010.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

Monthly-mean temperatures in both regions show maximum values in summer (July or August) and minimum values in winter (December or January). On the other side, the electricity demand presents different patterns in urban and rural areas. In Sancheong, the electric energy consumption presents a maximum value only in winter because of the use of electric heating appliances, while that in Seoul shows two peaks over a year because of the use of air conditioning as well as electric heating appliances. The electricity consumption in Seoul shows a maximum value in January, which decreases until May. Then, the electricity consumption begins to increase from the use of air conditioning systems until August. The electricity demand decreases slightly in October, the transition between summer and winter, and finally in November and December it increases again. In Seoul, the peak of electricity demand in summer is higher than that in winter.

b. Estimation of base temperature

The relationship between monthly electricity consumption and monthly-mean temperature in Seoul is shown in Fig. 4. In the raw data (Fig. 4, left), the relationship is U shaped, with variable effects due to human factors such as socioeconomic changes and technologic improvements, and the transition zone is located at approximately 15°–20°C. On the other hand, the detrended electricity demand data (Fig. 4, right) show a better correlation (V shape) with temperature. The relationship is nonlinear, with two maxima and one minimum. The base temperature computed using piecewise linear regression method was 17.1°C, which was determined from the sum of the average (12.8°C) of monthly-mean temperature and the transition temperature (4.3°C). This base temperature (17.1°C) is below the 18° and 26°C threshold that is customarily used in heating and cooling energy demand analysis. In a year, the winter season is longer than the summer season, indicating a wider response of electricity demand. However, the sensitivity of electricity demand to temperature is larger for the summer season (+86 403 MW h °C−1) than for the winter season (−26 017 MW h °C−1).

Fig. 4.
Fig. 4.

Relationship between monthly-mean temperature and monthly electricity consumption in Seoul for the period 2001–10. (left) Raw data. (right) Detrended data; line is piecewise regression.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

Figure 5 displays the relationship between monthly electricity demand and monthly-mean temperatures in the rural area of Sancheong. In the raw data, the electricity consumption generally shows a downward trend with increasing temperatures, indicating that energy is predominantly used for heating in this region. In contrast to the urban environment of Seoul, the relationship between detrended electricity demand and mean temperatures in Sancheong reveals an L shape with a slight skew. The transition point is shown at 16.3°C, which is the base temperature for HDD calculation of Sancheong. The electricity consumption at temperatures below 16.3°C increases proportionally, while that above 16.3°C shows no change. This depicts that the summer electricity demand in Sancheong is not influenced by high temperatures. The sensitivity of the electricity demand to the temperature change for the winter season is −225 MW h °C−1.

Fig. 5.
Fig. 5.

As in Fig. 4, but for Sancheong.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

Table 2 presents the base temperature and the sensitivity of electricity demand to temperature obtained by piecewise linear regression between the two datasets separating the winter and summer season for 35 regions. In general, the urban areas showed V-shaped temperature–energy consumption relationships, while the energy use–temperature function in rural areas was L shaped. This indicates that the electricity load in summer is influenced by the size of the local population, building stock, and overall economic activity in the region. Especially, the sensitivity of electricity demand to temperature in large urban areas is much greater for the summer season than for the winter season.

Table 2.

Regional base temperature (i.e., Tb) determined using the electric energy signature and the sensitivity (MW h °C−1) of electricity demand to temperature for 35 regions. The notations indicate a large urban station (LU), a small urban station (SU), and a rural station (R).

Table 2.

The regional base temperatures range was determined from 14.7° to 19.4°C. Figure 6 presents the relationship between the regional annual-mean temperatures and base temperatures. The correlation coefficient between them is r = 0.859 (p = 0.000), showing that the regional base temperatures increase as the annual-mean temperatures increase. This indicates that residential and commercial sectors in warm regions have higher balance point temperatures than those in cold regions, given historical adaptation in warm regions to the prevailing warmer climate. Therefore, potential impacts of climate change on the energy sector may be dependent on place-specific attribute.

Fig. 6.
Fig. 6.

Relationship between regional annual-mean temperatures and regional base temperatures (** indicates significant at p < 0.01).

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

c. HDD and CDD calculated by new base temperature

The HDD values, using regional relative base temperatures via energy signature and a base temperature commonly used, have been calculated and tabulated for 35 regions (Table 3). Annual HDD varies considerably from region to region. The highest value of HDD is 2556.2 for Yeongcheon, calculated using a base temperature that was determined using the energy signature, while the lowest value of HDD is 1861.9 for Jeju. This indicates that a resident living in Yeongcheon should spend more on fuel to enjoy the same heating comfort as a resident living on the island of Jeju. In most stations, the HDD calculated by new base temperatures are generally lower than the HDD with a base temperature of 18°C, except for some stations of the southern coast and island, such as Jeju, Tongyoung, Ulsan, Busan, and Pohang.

Table 3.

Annual HDD calculated from regional base temperatures determined in this study and a base temperature of 18°C used commonly for the period 2001–10.

Table 3.

Table 4 shows the regional CDD calculated from regional base temperatures via energy signatures and a base temperature of 26°C. The highest value of CDD is 912 for Suncheon at the new base temperature, while the lowest value of CDD is 542 for Tongyeong. This indicates that buildings need much more energy for cooling in Suncheon than in Tongyeong. The regional differences between CDDs calculated from new base temperatures and the base temperature of 26°C ranged from 514 to 885, indicating that the all calculated CDD from the new base temperatures are higher than those calculated with a base temperature of 26°C.

Table 4.

Annual CDD calculated from regional base temperature determined in this study and a base temperature of 26°C used commonly for the period 2001–10.

Table 4.

Figure 7 displays the time series of HDD and CDD in Seoul for the period 1971–2012. The temporal pattern of both HDDs calculated from the base temperatures of 17.1° and 18°C has shown a general decreasing trend since the mid-1980s, but a recent increase. The variability patterns of these HDD are very similar, while the CDD shows a different pattern on base temperatures. The CDD values with a base temperature of 26°C are very small and then show no significant trend. In addition, some of them have a zero value (e.g., year 1980) because of high base temperatures, in which case degree-days are a poor index for climate research. On the other hand, the CDD values with a base temperature of 17.1°C show a significantly increasing trend (+43.9°C days per decade).

Fig. 7.
Fig. 7.

Interannual variations in (left) heating degree-days during winter and (right) cooling degree-days during summer from 1971 to 2012 in Seoul.

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

The relationships between seasonal-mean temperature and seasonal HDD and CDD in Seoul from 1971 to 2012 are shown in Fig. 8. The correlation between mean temperature and HDD in the winter season (October–April) is r = 0.998 for the base temperature of 18°C, and r = 0.997 for the base temperature of 17.1°C. However, for the relation with mean temperature in the summer season (May–September), the correlation value of the CDD with the base temperatures of 17.1°C is higher than the CDD with the base temperatures of 26°C. This means that the CDD values with a base temperature of 17.1°C are a better index to reflect an increasing trend of summer temperature in Seoul than the CDD values with a base temperature of 26°C.

Fig. 8.
Fig. 8.

Linear relationship between (left) heating degree-days during winter and (right) cooling degree-days during summer and seasonal-mean temperature from 1971 to 2012 in Seoul (** indicates significant at p < 0.01).

Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0220.1

4. Summary and conclusions

The HDD and CDD are important climatic indicators that are widely used in assessing the impact of climate change on future energy demand and in monitoring energy performance. This method comprises a summation of the difference between the outdoor and base temperature over a specific time interval. The base temperature is related to the desired indoor temperature, which is further influenced by factors such as lifestyle and income, and may vary with the region. The aim of this study was to estimate the real base temperature for regional HDD and CDD calculation in South Korea. The regional base temperatures are determined quantitatively through a statistically based technique using monthly electricity consumption and monthly-mean temperature data during the period 2001–10.

First, this study shows that the residential and commercial electricity consumption increases rapidly in recent decades because of socioeconomic factors. The overall upward trends are due to changes in the size of the local population, building stock, and increased proliferation of electric heating and air conditioning units, as well as increases in overall economic activity in the region. The electricity consumption exhibits an annual cycle, but it shows different patterns depending on region. Peaks of electricity demand in urban areas are observed for both summer and winter, coinciding with maximum and minimum of temperatures within a year, while electricity consumption in rural areas show a single peak, as a representation of winter heating.

Next, this study found that the relationship between monthly electricity consumption and monthly-mean temperatures is nonlinear, through the energy signature method. Winter and summer seasons were separated, allowing for a better quantification and characterization of the electricity consumption functions. However, the regional patterns differed greatly. They generally show a V-shaped energy use–temperature relationship in urban and southern coastal areas, but an L-shaped relationship in rural areas. In most urban areas, there are significant negative correlations between monthly-mean temperature and the monthly electricity consumption in winter, while there are significant positive correlations between them in summer. In most rural areas, there are significant negative correlations between them in the winter season, but there are no changes in summer. The South Korean base temperature, which means the transition point of electricity demand for from heating to cooling (or no change), is in the range of 14.7°–19.4°C.

Finally, the consequences of HDD and CDD calculated based on a base temperature determined in this study were assessed by comparing with values calculated from an existing base temperature. The regional HDD and CDD as calculated using base temperatures determined through this study were 1861.9–2556.2 and 542.0–912.1, respectively, and those calculated from the previous base temperature were 1560.9–3091.4 and 18.0–92.6, respectively. The HDD values were similar because of the small difference between two base temperatures. The CDD value calculated from this study represents better the regional climatic characteristics and change than CDD with a base temperature of 26°C, and hence should be a better climatic indicator.

The sensitivity of electricity demand to temperature in summer is apparent only in urban and southern coastal areas, since most rural areas maintain sufficiently comfortable conditions without air conditioning, largely because, in rural areas, more work takes place outdoors than that in an urban environment. However, a growth in the usage of air conditioning will occur in rural settings with further increases in temperatures due to global climate change. The development of air conditioning will also have consequences for the annual pattern of energy demand, with the current peak in electricity consumption during winter becoming much less pronounced. Although space heating is still the dominant electricity demand for buildings in most regions except for some large urban areas, special attention should be paid to space cooling, since the cooling demand is expected to grow rapidly both in urban areas and in rural areas. Therefore, the knowledge of heating and cooling requirements calculated from the true regional base temperature will play a significant role for estimating the potential change in regional electricity consumption as a consequence of climate change.

Acknowledgments

We thank anonymous reviewers for their helpful comments. This work was supported by project “NIMR-2012-B-2 (Development and Application of Methodology for Climate Change Prediction)” of the National Institute of Meteorological Research.

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    • Search Google Scholar
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    • Search Google Scholar
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  • Fig. 1.

    Locations of 35 observational stations in South Korea with their urban–rural area breakdown. The stations with their names and numbers are listed in Table 1.

  • Fig. 2.

    Theoretical relationship between temperature and energy use.

  • Fig. 3.

    (left) Monthly electricity consumption (MW h) and (right) monthly-mean temperature (°C) in (top) Seoul and (bottom) Sancheong from January 2001 to December 2010.

  • Fig. 4.

    Relationship between monthly-mean temperature and monthly electricity consumption in Seoul for the period 2001–10. (left) Raw data. (right) Detrended data; line is piecewise regression.

  • Fig. 5.

    As in Fig. 4, but for Sancheong.

  • Fig. 6.

    Relationship between regional annual-mean temperatures and regional base temperatures (** indicates significant at p < 0.01).

  • Fig. 7.

    Interannual variations in (left) heating degree-days during winter and (right) cooling degree-days during summer from 1971 to 2012 in Seoul.

  • Fig. 8.

    Linear relationship between (left) heating degree-days during winter and (right) cooling degree-days during summer and seasonal-mean temperature from 1971 to 2012 in Seoul (** indicates significant at p < 0.01).

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