Per capita electricity consumption of Bangladesh is 400 KWh. Of the total population of 160 million, only 40 percent has the access of using electricity. Dhaka city consumes about 40 - 45 percent of the total electricity generation of the country. This study reports the trend of electricity use in the Dhaka city with emphasis on the impact of changing temperature due to urbanization and weather change. Hourly data of electricity demand of Dhaka city and the temperature profile of the city for a period of thirty months have been used for this study. To relate weather data like temperature, humidity, wind speed, wind direction, atmospheric pressure, dew point and visibility etc. with electricity demand of the city about 16,508 data between 2011 and 2017 have been considered. A statistical regression has been done to establish a relation between them. From this study it is found that reduction of only 1 °C air temperature could save 81 MV of electricity consumption in Dhaka city. A time series forecast has been done to estimate probable temperature change and subsequent electricity consumption up to year 2020. From the study it has been established that the temperature dependence of electricity consumption in Dhaka city is about 75%.
Dhaka, the capital of Bangladesh is a densely populated city. It is one of the fastest growing mega cities of the world. Sixteen million people live within the area [
The electrical energy consumption of a city is closely related to its ambient temperature [
To examine factors influencing energy consumptions, many studies have been conducted. Some of them [
The methodology is empirical and statistical. A multiple linear regression was first carried out to explore the dependence of the monthly consumption of each energy product on meteorological parameters. Five meteorological variables were selected. They are ambient temperature, relative humility (rainfall related), wind speed, wind direction, and precipitation. Pearson’s correlation and covariance [
Taking electricity demand as a function of temperature, the demand can be calculated as
Y ( hour , x ) = m e D . x (1)
For every 1˚C rise of temperature the electricity demand would be
Y ( hour , x + 1 ) = m . e D . ( x + 1 ) (2)
The demand reduction due to decrase of 1˚C of weather temperature would be
Δ Y = Y ( hour , x + 1 ) − Y ( hour , x ) (3)
Real time hourly data of ambient temperature form year 2011 to year 2017 has been collected from Bangladesh Meteorological Department. Using these values a time series analysis has been done to estimate probable temperature situation up to year 2020 (
Year | Mean monthly maximum temperature (˚C) | Yearly average | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |||
2011 | 20.5 | 25.5 | 28.5 | 30.5 | 31 | 30 | 29.5 | 29 | 29 | 29.5 | 26 | 23.5 | 27.71 | |
2012 | 21 | 25.5 | 28.5 | 30.5 | 32.5 | 31.5 | 30 | 29 | 29 | 28 | 25 | 21 | 27.63 | |
2013 | 22 | 26 | 30 | 32 | 30.5 | 31 | 30 | 29.5 | 30 | 28 | 27 | 24.5 | 28.38 | |
2014 | 23.5 | 26 | 30 | 33.5 | 34 | 32.5 | 31 | 30 | 30.5 | 29 | 28.5 | 24.5 | 29.42 | |
2015 | 24 | 26.5 | 30 | 33 | 33 | 32 | 30 | 30.5 | 32 | 29.5 | 28 | 25 | 29.46 | |
2016 | 25 | 27.5 | 31 | 34 | 33.5 | 32 | 31 | 31 | 31 | 30.5 | 27.5 | 26 | 30 | |
2017 | 25.3 | 27.8 | 31.3 | 34.3 | 33.8 | 32.3 | 31.3 | 31.3 | 31.3 | 30.8 | 27.8 | 26.3 | 30.3 | |
2018 | 25.7 | 28.2 | 31.7 | 34.7 | 34.2 | 32.7 | 31.7 | 31.7 | 31.7 | 31.2 | 28.2 | 26.7 | 30.7 | |
2019 | 26.2 | 28.7 | 32.2 | 35.2 | 34.7 | 33.2 | 32.2 | 32.2 | 32.2 | 31.7 | 28.7 | 27.2 | 31.2 | |
2020 | 26.7 | 29.2 | 32.7 | 35.7 | 35.2 | 33.7 | 32.7 | 32.7 | 32.7 | 32.2 | 29.2 | 27.7 | 31.7 | |
Monthly average | 23.9 | 27.09 | 30.59 | 33.34 | 33.24 | 32.09 | 30.94 | 30.69 | 30.94 | 30.04 | 27.59 | 25.24 | ||
GDP growth, population, increase of cooling appliances and price per unit of electricity. Due to lack of data at regional level, the effect of time-varying variables has been neglected. Lastly the study does not include other effects of climate change which might indirectly influence electricity demand, such as changes in wealth and energy efficiency, new technologies etc.
Bangladesh has electricity production capacity of 15,761 MW (Source: Power Grid Company of Bangladesh Limited, PGCBL). BPDB has taken a massive capacity expansion plan to add about 11,600 MW generation capacity in next 5 years to achieve 24,000 MW capacity with the aim to provide quality and reliable electricity to all Bangladeshi people. The power system is being expanded to keep pace with the fast growing demand, he added. Sources said the number of power connection receivers in Bangladesh have risen to some 26 million so far year 2017. Despite a robust rise in the capacity, the official who declined to be named, said the current power generation is, to some extent, insufficient to meet increasing demand. The total power production of Bangladesh varies within 10,000 - 13,000 MW (including captive) in hot summer days, and 7000 - 8500 MW in winter days. Dhaka city consumes 4000 - 4500 MW electricity alone, which is about 40 percent of the total.
Weather condition plays a vital role in electricity demand all over the world. As Bangladesh is a hot, humid and moist country, ambient temperature is vital issue for power demand.
In general, power consumption increases when temperature rises in Dhaka city. This is also true for Bangladesh as a whole. The following curve fitting figures (Figures 5-8) revealed that the temperature and demand are highly inter-related.
Tables 2-5 show the Pearson’s correlation and covariance result between two variables.
As shown in
electricity demand, the overall prediction equation for electricity consumption on a single hour can be shown by following equation.
y = 747.75 e 0.0398 x (4)
where, X is the mean monthly maximum temperature and Y is the electricity demand of the Dhaka city. Being the best curve fitted model Equation (4) has been used for peak power demand computation corresponding to threshold temperatures and depicted in
Using Equation (4) for corresponding threshold temperatures, electricity demand computed.
Dhaka city electricity load is highly seasonal variation dependent. Load peak in the summer is almost two times higher than the winter. However, we can divide Dhaka city in two different parts to show weather fluctuations, warm period (March to October) and cold and moderate period (November to February). Cooling load is the most effective factor in using electricity and load peak in network with the use of cooling devises in warm period. In this paper, the effects of temperature changes on electricity demand and load variations are studied. In the study load variation index and temperature correlation coefficient are calculated.
The temperature rise in Dhaka city is most prominent in the months of March, April and May. In March the temperature is between 26˚C - 32˚C with a
Covariance | Pearson’s correlation | |||
---|---|---|---|---|
Temperature | Electricity Demand | Temperature | Electricity Demand | |
Temperature | 23.46949 | 1 | ||
Electricity Demand | 1869.588 | 259463.5 | 0.757578 | 1 |
Month | Equation | Constant (m) | R square value | P value |
---|---|---|---|---|
January | y = 938.33e0.0315x | 938.33 | R2 = 0.3306 | 0.00 |
February | y = 888.41e0.0336x | 888.41 | R2 = 0.4475 | 0.00 |
March | y = 1057.8e0.0298x | 1057.8 | R2 = 0.4718 | 0.00 |
April | y = 1604.5e0.0177x | 1604.5 | R2 = 0.3754 | 0.00 |
May | y = 1029.8e0.0314x | 1029.8 | R2 = 0.3409 | 0.00 |
June | y = 1606.4e0.02x | 1606.4 | R2 = 0.2821 | 0.00 |
July | y = 326.03e0.0666x | 1455.2 | R2 = 0.3689 | 0.00 |
August | y = 1349.2e0.0377x | 1349.2 | R2 = 0.3352 | 0.00 |
September | y = 1513.6e0.0152x | 1513.6 | R2 = 0.5032 | 0.00 |
October | y = 1035.4e0.0296x | 1035.4 | R2 = 0.2995 | 0.00 |
November | y = 775.56e0.0385x | 775.56 | R2 = 0.4594 | 0.00 |
December | y = 840.88e0.0345x | 840.88 | R2 = 0.4428 | 0.00 |
Overall | y = 747.75e0.0398x | 747.75 | R2 = 0.5884 | 0.00 |
Month | Correlation coefficient | Month | Correlation coefficient |
---|---|---|---|
January | 0.565750467 | July | 0.631738 |
February | 0.663254 | August | 0.578586577 |
March | 0.690076 | September | 0.514007602 |
April | 0.698475353 | October | 0.542152313 |
May | 0.602025785 | November | 0.666151055 |
June | 0.531974368 | December | 0.652583771 |
Overall | 0.7575781 |
Year | Electricity consumption (MWh) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | ||
2014 | 1209,361 | 1173,555 | 1571,965 | 2042,366 | 2152,864 | 1984,005 | 1840,256 | 1785,349 | 1796,106 | 1644,389 | 1476,123 | 1351,784 | |
2015 | 1296,571 | 1265,158 | 1721,913 | 1707,700 | 1988,717 | 1975,042 | 1812,046 | 2055,334 | 1872,785 | 1958,733 | 1660,048 | 1461,365 | |
2016 | 1451,450 | 1563,573 | 1970,286 | 2130,994 | 2055,241 | 2281,348 | 1991,819 | 1991,830 | 1848,934 | 2072,920 | 1608,511 | 1565,803 |
2017 | 1522,782 | 1519,310 | 1933,514 | 2108,442 | 2135,795 | 1947,116 | 1933,514 | 1933,514 | 1871,142 | 1895,417 | 1627,832 | 1584,611 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
2018 | 1547,219 | 1543,691 | 1964,542 | 2142,277 | 2170,069 | 1939,382 | 1964,542 | 1964,542 | 1901,170 | 1925,834 | 1653,955 | 1610,040 |
2019 | 1578,317 | 1574,718 | 2004,028 | 2185,336 | 2213,686 | 2018,125 | 2004,028 | 2004,028 | 1939,381 | 1964,542 | 1687,198 | 1925,834 |
2020 | 1610,040 | 1630,958 | 2044,307 | 2229,259 | 2258,180 | 2058,689 | 2044,307 | 2044,307 | 1978,362 | 2004,028 | 1721,109 | 1675,412 |
mean of 30˚C. Correlation coefficient during this month is 0.690076 (
During June, the average temperature increases. Due to rainy season precipitation level is high as the humidity is high during the month of June. Humidity is inversely proportional to electricity demand and the correlation coefficient is lowest between the variables. Despite of poor R square value, correlation coefficient supports the relation between electricity demand and temperature. Same is true for the month of October.
The average temperature of Dhaka city increases during July and August without significant increase in the electricity network load. The reason is high precipitation. Correlation coefficient are 0.631738 and 0.578586577 during the months of July and August respectively while R square value is only 0.2089 and 0.3352. Warm period ends during September and October and the average temperature falls. Correlation coefficient are 0.5140067 and 0.542152313 during the months of September and October.
Cold period of the year starts in Dhaka from early November. Change in the temperature level occurs from 26˚C to 18˚C and it starts moderate weather conditions all over Dhaka city. Excess load which was caused by using electricity for cooling systems reduces during this period. Mild weather conditions reduce electricity network load amount. Regression equation shows a decrease of electricity consumption in the months of November, December, January and February.
From regression analysis shown in Figures 5-8, Equation (4) (
y = 747.75 e 0.0398 x (4)
Change in 1˚C temperature changes the power consumption (MW) as calculated by Equation (3)
If temperature decreases from 30˚ to 29˚ Celsius energy saving for an individual hour would be
After finding the power change for different range of temperature, it is found that average change in power is about 81 MW for 1˚ change in temperature. Change of the air temperature is the reason of load dependence on environment temperature and causes a increase on network load. An overall correlation coefficient between load and temperature has been found to be 0.7575781 during this study.
There is a strong relationship between changes in the temperature and electricity consumption. Based on the data of power consumption for a last 47 months of the City of Dhaka in Bangladesh, it can be concluded that electricity consumption changes in response to change in the mean daily air temperature. The peak electricity consumption is significantly increased during summer season as compared to winter season due to the uses of cooling appliances. This paper forecasts that the mean air temperature of Dhaka city will be increased 2 - 3 degree celsius on an average during next couple of years. This study proved that reduction of 1˚C air temperature reduces about 81 MW electricity demand of Dhaka city. The forecasted values of monthly electricity demand depicts that electricity consumption is more in hot summer season and highest consumption would be 2258.180 (GWh) in May, 2020 due to increase in temperature. The average monthly behavior of forecast values of temperature and electricity reveals that the maximum temperature has a great influence on electricity consumption keeping other parameters constant.
The study excludes the discussion of different micro and macroeconomic factors which highly controls the electricity changes behavior. Different analysis could be done to estimate a cross temperature response function, which can represent the effect of non-climate variables on electricity demand at different temperatures. A conventional top-down approach could be followed which needs a thorough and accurate database including a detailed breakdown of appliance categories, their numbers at different years and their power rating at different temperatures. The effect of relative price of electricity per KWh analysis could be significant for heating demand. Moreover, technical progress in electric appliances and changes in consumption habits (proxied by the time trend) analysis would find some more accurate result. Combination of climate and non-climate factors can represent a better estimation.
However, temperature change is not the only factor of the electricity demand change. As stated earlier electricity demand changes with humidity, dew points, wind speed, wind direction and other macro and micro economic factors like GDP growth, population, increase of cooling appliances, price per unit of electricity, ease to access electricity connections in different area etc. However, overall Bangladesh contains high humidity throughout the year. It remains 85% to 95% throughout the year. Moreover, climate variables are more or less interrelated to each other. Thus a 360 degree analysis could find a better result. This will need a three dimensional graphical representation to find the relationship between all the climate and non-climate variables and electricity demand changes. To avoid the higher order complexity this paper avoided the other variables which also have a significant role in electricity consumption change. Further work will define compiling the other variables we hope.
The kind support from Bangladesh Meteorological Department is highly acknowledged. We also deeply thank Dr. Md. Farid Ahmmed of Power Grid Company of Bangladesh Ltd. (PGCBL) for helping with electricity demand data.
Istiaque, A. and Khan, S.I. (2018) Impact of Ambient Temperature on Electricity Demand of Dhaka City of Bangladesh. Energy and Power Engineering, 10, 319-331. https://doi.org/10.4236/epe.2018.107020
• The p-value is defined informally as the probability of obtaining a result equal to or “more extreme” than what was actually observed, when the null hypothesis is true.
• R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination and R-squared = Explained variation/Total variation.
• The diagonal of a covariance matrix provides the variance of each individual variable: covariance with itself and the off-diagonal entries in the matrix provide the covariance between each variable pair. The positive value determines positive relation between two variables and vice versa.