a case study on demand forcasat

8/6/2019 A Case Study on Demand Forcasat

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A CASE STUDY ON DEMAND FORCASAT: ELECTRICITY IN BANGLADESH

ABSTRACT:

IN this case study we had tried to figure out the demand of service on future periods.

For this we had select electricity sector of Bangladesh. We had collect data of demand on

previous periods and had utilized it for projecting future demands. We used various

quantitative methods like time series and trend analysis for predicting future demands and

different error analysis also included. The results of the study are useful for the energy

planning of the country.

1. INTRODUCTION:Bangladesh is a country under development and moving towards the industrialization.

Although Bangladesh is predominantly an agricultural country but a large number oflarge-scale Industries based on both indigenous and imported raw materials have been set

up. With huge population and limited resources Bangladesh is facing notable crisis ofenergy. Energy is the main factor that makes every machinery run and electricity is the

main source of energy. Day by day the demand for electricity is increasing but production

is not enough. Effort on decreasing System losses and increasing production capacity has

been a major matter to be focused for Bangladesh.

2. Current situation of Bangladesh:

Only around 20% of the population (25% in urban areas and 10% in rural areas)

has access to electricity, and per capita commercial energy consumption is amongthe lowest in the world.

Surface Area: 144, 0 1000 sq kmPopulation: 164, 7 Millions of inhabitants - 2009 (estimates after 2007)

Current GDP: 67, 8 Billions of Euros - 2009 (estimates after 2008)

GDP per capita: 411, 4 Euros - 2009 (estimates after 2007)

Electricity - production by source:

fossil fuel: 93.7%

hydro: 6.3%

nuclear: 0%other: 0% (2001)

Electricity - consumption: 14.25 billion kWh (2001)


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3. About forecasting:

Forecasting is the process of making statements about events whose actual outcomes

(typically) have not yet been observed. A commonplace example might be estimation ofthe expected value for some variable of interest at some specified future date. Prediction

is a similar, but more general term. Both might refer to formal statistical methods

employing time series, cross-sectional or longitudinal data, or alternatively to less formal

judgmental methods. Usage can differ between areas of application: for example in

hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of

values at certain specific future times, while the term "prediction" is used for more

general estimates, such as the number of times floods will occur over a long period. Risk

and uncertainty are central to forecasting and prediction; it is generally considered goodpractice to indicate the degree of uncertainty attaching to forecasts. The process of

climate change and increasing energy prices has led to the usage of Egain Forecasting of

buildings. The method uses Forecasting to reduce the energy needed to heat the building,thus reducing the emission of greenhouse gases. Forecasting is used in the practice of

Customer Demand Planning in every day business forecasting for manufacturingcompanies. The discipline of demand planning, also sometimes referred to as supply

chain forecasting, embraces both statistical forecasting and a consensus process. An

important, albeit often ignored aspect of forecasting, is the relationship it holds with

planning. Forecasting can be described as predicting what the future will look like,whereas planning predicts what the future shouldlook like.There is no single right

forecasting method to use. Selection of a method should be based on your objectives and

your conditions (data etc.).A good place to find a method, is by visiting a selection tree.

An example of a selection tree can be found here.

There are four steps to forecasting:

1

Set objectives for forecast - what decisions will forecast influence, and how - what

aspects of plan are vulnerable to surprise - how accurate must forecast be to be of any

use?

2 Obtain forecast - from existing sources or commissioned study.

3

Evaluate forecast, including assessing the sensitivity of planned actions on the

accuracy of the forecast - what exactly is being forecast - what assumptions does the

forecast itself make - source of forecast and past reliability - what objectives of

forecasters, and how do they compare with your own?

4 Disseminate the forecast, persuade others of its accuracy and appropriateness.


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4.FORECASTING TECHNIQUES WE HAVE USED:

FOR the projection of future demand on the data we had collected, we find time series

analysis and casual model best fits. Forecast model we selected is illustrated on following

flow chart.

4.1 Time series analysis:

4.1.1 Simple moving average: Moving average techniques forecast demand bycalculating an average of actual demands from a specified number of prior periods each

new forecast drops the demand in the oldest period and replaces it with the demand in the

most recent period; thus, the data in the calculation "moves" over time

Time series analysis Casual model

Simple moving average Linear regression

TRENDANALYSIS

DATA COLLECTION

Fig; Forecasting model


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simple moving average:

where N = total number of periods in the average

forecast for period t+1: Ft+1 = At

Key Decision: N - How many periods should be considered in the forecast

Tradeoff: Higher value of N - greater smoothing, lower responsiveness

Lower value of N - less smoothing, more responsiveness

- the more periods (N) over which the moving average is calculated, the less susceptible

the forecast is to random variations, but the less responsive it is to changes

- a large value of N is appropriate if the underlying pattern of demand is stable

- a smaller value of N is appropriate if the underlying pattern is changing or if it is

important to identify short-term

4.1.2 Trend analysis: There are no proven "automatic" techniques to identify trendcomponents in the time series data; however, as long as the trend is monotonous

(consistently increasing or decreasing) that part of data analysis is typically not very

difficult. If the time series data contain considerable error, then the first step in the

process of trend identification is smoothing.

Smoothing. Smoothing always involves some form of local averaging of data such that

the nonsystematic components of individual observations cancel each other out. The most

common technique is moving average smoothing which replaces each element of theseries by either the simple or weighted average ofn surrounding elements, where n is thewidth of the smoothing "window" (see Box & Jenkins, 1976; Velleman & Hoaglin,

1981). Medians can be used instead of means. The main advantage of median as

compared to moving average smoothing is that its results are less biased by outliers(within the smoothing window). Thus, if there are outliers in the data (e.g., due to

measurement errors), median smoothing typically produces smoother or at least more

"reliable" curves than moving average based on the same window width. The main

disadvantage of median smoothing is that in the absence of clear outliers it may produce

more "jagged" curves than moving average and it does not allow for weighting.


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In the relatively less common cases (in time series data), when the measurement error is

very large, the distance weighted least squares smoothing or negative exponentiallyweighted smoothing techniques can be used. All those methods will filter out the noise

and convert the data into a smooth curve that is relatively unbiased by outliers (see therespective sections on each of those methods for more details). Series with relatively few

and systematically distributed points can be smoothed with bicubic splines.

Fitting a function. Many monotonous time series data can be adequately approximated

by a linear function; if there is a clear monotonous nonlinear component, the data first

need to be transformed to remove the nonlinearity. Usually a logarithmic, exponential, or

(less often) polynomial function can be used.

4.2 Casual modeling:

A reliable method of prediction becomes possible if you have, through research, obtained

a model which not only describes (as in the previous section) the development of thephenomenon to be predicted but also explains it, in other words enumerates the reasons

why it happens. In the best case the reasons and their outcomes are assembled as a model

defining the dynamic invariance of change in the process to be predicted.

The weather, for example, need today no more be predicted on the basis of a statisticalassociation of air pressure and weather only. The science of meteorology has lately

advanced so much that we now know and can make use of the invariable structure ofmoving cyclones which explains the changes both in air pressure and in weather. Even

the proverb about red skies has now been given an explanation:

"Because the weather patterns in North America generally move from west to east, when

clouds arrive overhead at sunrise the sky will appear red, signalling a storm "moving in".When the storm eventually passes, the sky will clear in the western sky. If sunset occurs

simultaneously, the light will cast a red glow on the clouds above, now moving towardsthe east."

The most elementary method of forecasting on the basis of a causal model is to use the

model just like a statistical association, explained earlier. This is particularly easy when

one of the variables in the model is time: then you just insert the right year into the

model, and it immediately becomes the desired forecast.

If time is not included in the causal model, the model may still be helpful, because you

can often predict the development of the independent variable easier than the future of the

dependent variable or of the entire system - not least because of the fact that a reason

normally precedes its result and it is thus not so distant in the future as the outcome will

be.


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When you know the causal relationships between the variables you will be able to use

much more advanced methods of forecasting than mere statistical models would permit.These include:

You can better assess whether the model remains valid also in the future.

You can assess with sensitivity analysis the probable error of the forecast.

You will also be able to modify the model, with a high degree of reliability,

according to the requirements of the situation.

If you want not only to forecast but also to change the future, you can pinpoint

those changes in the independent variables that are needed to cause the desired

change in the dependent variables.

The causal model is often so complicated

that it is best managed by using a

computer. Even then, you will usually needan illustrative presentation of your modelto clarify your thinking and finally to be

presented in the report. In such an

illustration you will need a notation systemto describe the various logical relations

between the variables. The computer

program will often be able to print out the

model, using its in-built notations. If you

can find no suitable ready made notation

systems, you can devise one.

5.2.1 Linear regression: simple linear regression is fitting a straight line to a set of

paired observation.Mathematical expression for straight line is

Y=mx+c

Where

Y=dependent variable

m=slope of fitted linex=independent variable

c=intercept on y axis

This technique of fitting straight line can be used as forecasting model by plotting

independent variable to figure out dependent variable. Slop and intercept of fitted line canbe determined by following formula


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m=(nxiyi-xiyi)/(nxi2-(xi)

2) and

c=yi/n-mxi/n

5.3 Error calculations techniques:

5.3.1 Mean absolute deviation:

=

=

n

t

tt FDn

MAD1

1

Wheren=number of observation

Dt=current demandFt=current forecast

5.3.2 Mean square error

2

1

)(1

t

n

t

t FDn

MSE = =

Where


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n=number of observation

Dt=current demandFt=current forecast

5.3.3 Mean absolute percentage error:

=

=

n

t t

tt

D

FD

nMAPE

1

100

Wheren=number of observation

Dt=current demand

Ft=current forecast

5.3.4 Cumulative Sum of Forecast Error:

5.3.5 Mean forecast error:

)(1

1 t

n

t t

FDn

MFE =

=

5.4 Tracking signal:

6.OUR STEPWISE OPERATIONS:

6.1 DATA COLLECTION;

We had collected all data used in our forecasting model from published e-books,

Bangladesh bureau of statistics and websites. We collected data on population, electricityproduction, system loss and load shedding previous periods.


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Fig: Data obtained

6.2 FORMULATION OF DATA: We then calculate shortage and demand from this data.


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Fig: Calculated demand and shortage

Fig: demand vs population

6.3 TIMESERIES ANALYSIS:

6.3.1 Simple moving average:

We plotted obtained data with reference of time and used simple moving average of one

period to forecast short period demand in future.


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Fig: Simple moving average

Fig; Excel sheet for calculation of single period moving average


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6.3.2 Trend analysis: Further trend analysis had been done to obtain forecasting on

long horizon.

Fig; Excel sheet for trend analysis;

Fig; Excel sheet for trend analysis;


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Fig : Trend analysis

Fig: Yearly increasing demand


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6.3 Casual model:6.3.1 Linear regression:

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6.3.2 Demand vs. population regression analysis:

Fig: Excel sheet

Fig: scatter plot demand vs.. Population

Fig: After adding trend line


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Conclusion :The most sensitive issue corresponding to modern human civilization is electricity

except which everything will be destroyed. By our forecasting case study we haveevaluate trend of electricity demand after 14th periods (2020) is nearly seven thousand

megawatt per hour. Due to the lacking of exact data of previous periods our forecasted

model might not be totally reliable. Our forecasting model is enough able to demonstrate

the demand patterns of electricity.

References:

1.Bangladesh power sector data bookPrepared by: Engr. Abdur Rouf, Engr. KM Nayeem Khan, Engr. Shazibul HoquePublished date: june 2006

2.3rd International Conference on Electrical & Computer EngineeringICECE 2004, 28-30 December 2004, Dhaka, Bangladesh

3. Source: IndexMundi.com

Software used for calculation and forecasting: Microsoft excel

a case study on demand forcasat

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