# Presentation: 110472

## India Group Report

- 5-9 January 2004
- Pokhara, Nepal

- Regional Workshop on Advanced Forecasting & Modeling Techniques

##  EPS FORECASTCATEGORIES OF CONSUMPTION & THE METHOD USED FOR FORECASTING  :

_**CATEGORY**_**                                                **_**METHOD**_**DOMESTIC                                                       ****COMMERCIAL & MISC.                                ****PUBLIC LIGHTING ****	****	****	****	****TIME SERIES ****PUBLIC WATER WORKS                            ****LT INDUSTRIES                                              ****HT INDUSTRIES LESS THAN 1 MW           ****IRRIGATION                                                 ****LIFT IRRIGATION                                          ****HT INDUSTRIES 1 MW AND ABOVE ****	****	****END USE ****RAILWAY TRACTION ****BULK NON-INDUSTRIAL ****such as research establishments,port trusts, MES, construction power to power  projects **

** ****EPS FORECAST****CATEGORIES OF CONSUMPTION & THE METHOD USED FOR FORECASTING  :**

##  DEGREE OF ACCURACY

** ****DEGREE OF ACCURACY**
**OF **
**16****TH**** EPS PROJECTIONS**

- THE VARIATION BETWEEN PROJECTIONS OF ENERGY REQUIREMENTS AS PER  16th EPS AND ACTUAL UNRESTRICED REQUIREMENTS AT ALL INDIA LEVEL IS AROUND (+/-) 3%.

## METHODOLOGY OF FORECAST

- Basically Energy Based Projection
- Partial End-Use Method For Short-Term Projection
- Disaggregate Demand into Smaller Components
- End-Users Considered
- Domestic
- Commercial
- Public Lighting
- Public Water Works
- Irrigation
- Industrial
- Railway Traction
- Bulk supply to Non-Industrial Consumers
- Forecast For Each Category
- Total Energy Consumption by Aggregating Category-wise Projections
- Counterchecked by econometric modelling

## Model Description

- Model aims to project energy consumption for the country
- Model specifically aims at exploring the best fit estimate of energy consumption resulting from economic growth

## Model Description

- Model was chosen to test the relevance of Econometric model in estimating demand
- Result of the Model would also be compared with projections of 16th Electric Power Survey.

## Model Data

- Data for Electricity Consumption and GDP for the period 1950-2002 is used.

## Assumption for forecasting electricity consumption

- GDP is assumed to grow at 6.5 % till 2007 and 7% thereafter till 2011.

## Model Data

- Data availability was limited
- Only GDP information was available though we would have preferred to include price index and sectoral components of GDP
- Projection based on a sample from 1970-2002 gave a better fit as compared to 1950-2002

## Projection Methodology

- Linear function used to forecast energy consumption
- Yes, the forecast was based on actual data available as compared to estimates

## Estimated Models

- Following models were estimated,
- Elec = c + b * GDP
- Log(Elec) = c1 + b1 * log(GDP)

## Linear Model Estimation Results

- Dependent Variable: CONSUMPTION
- Method: Least Squares
- Date: 01/08/04   Time: 10:16
- Sample: 1970 2002
- Included observations: 33
- Variable	Coefficient	Std. Error	t-Statistic	Prob.
- GDP	0.307652	0.010803	28.47820	0.0000
- C	-35477.19	7722.392	-4.594067	0.0001
- R-squared	0.963183	    Mean dependent var		162452.9
- Adjusted R-squared	0.961996	    S.D. dependent var		99185.27
- S.E. of regression	19335.89	    Akaike info criterion		22.63601
- Sum squared resid	1.16E+10	    Schwarz criterion		22.72670
- Log likelihood	-371.4941	    F-statistic		811.0076
- Durbin-Watson stat	0.183787	    Prob(F-statistic)		0.000000

## Log Model Estimation Results

- Dependent Variable: LOG(CONSUMPTION)
- Method: Least Squares
- Date: 01/08/04   Time: 10:31
- Sample: 1970 2002
- Included observations: 33
- Variable	Coefficient	Std. Error	t-Statistic	Prob.
- LOG(GDP)	1.398668	0.044731	31.26813	0.0000
- C	-6.758486	0.593611	-11.38538	0.0000
- R-squared	0.969267	    Mean dependent var		11.79095
- Adjusted R-squared	0.968276	    S.D. dependent var		0.678677
- S.E. of regression	0.120881	    Akaike info criterion		-1.329329
- Sum squared resid	0.452978	    Schwarz criterion		-1.238632
- Log likelihood	23.93393	    F-statistic		977.6957
- Durbin-Watson stat	0.158871	    Prob(F-statistic)		0.000000

## GDP Elasticity

- 			LIN		LOG
- 2002	1.237	1.39
- Note: elasticity same for all years in case of log model.

## Forecasting Using Linear & Log Models

## Comparison with 16th EPS

- Forecasted Consumption in 2006-07
- 			LIN		LOG		16th EPS
- 2007	529612	670131	503367.9
- 2011	705239	978481	682655.4

## Thank You.