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Demand Forecasting

Demand Side Management (DSM) and supply side options both are level playing fields; whereas conventional power planning is supply focused and does not adequately include DSM options. With the advent of increasing growth in DSM efforts, it is essential that planners, engineers, and consultants continue to investigate the technical potential for DSM savings.

Demand forecast exercise to estimate the potential DSM options is in fact energy services projections after consideration of the technological basis that provide energy services (e.g., the type of lighting fixture, type of motors) in the projected year, as well as the socio-economic factors that determine the required levels of energy services.

Role of Demand Forecasting in Integrated Resource Planning:

With increasing DSM awareness modern utility planning should evolve to integrate a broader range of technological options including decentralised and non utility generating sources as well as energy efficiency and DSM options. This will also leads to integrating broader range of cost components, including environmental and other social cost in to the evaluation and selection of potential DSM Option. Integrated least cost planning frame work is provided in figure 1 below,

 

Effective least cost planning is driven by energy and peak demand forecast, because it helps to evaluate the need for new power supply resources. However, disaggregated demand forecast will help to determine which DSM and efficiency programs are worth pursuing and when, as well as in which sectors and end uses they should be implemented.

Demand forecast exercise helps in quantifying the potential resources available through various DSM options. The sector/ end use wise potential can be estimated based on the difference between the existing, or Business as Usual scenario’s (baseline, scenario's ) energy efficiency and a higher efficiency scenario. The Business as Usual scenario is noting but the "frozen efficiency" scenario which is simply a forecast of energy-service growth with no efficiency improvement, or it can include expected changes in energy efficiency in present situation.


Role of Demand Forecasting in Integrated Resource Planning:

Forecasting load demand is a difficult procedure and combines art with science. While forecasting tools are available to aid in the process, the key contributions of forecasters are their knowledge of electricity consumers and an understanding of the way they use electricity and other competing energy forms. Overall demand forecast framework and steps involved are shown in Figure 2.

 

Demand Forecasting Steps

STEP 1: Demand Forecasting Objective

The objective and scope of the forecasting exercise would be specific and different for each of the stakeholders in power business and is prepared to meet their requirements. The forecast prepared by the distribution entity could be drawn from the individual consumer segment or end use forecasts. It gains significant importance considering its relevance for the least cost integrated planning which includes DSM options along with the utility supply options as well as to asses the impact of environmental and social costs.

The demand forecasting exercise assumes greater significance in the context of introduction of energy efficient technologies, awareness of environmental and social impact of energy, power exchange, competition and open access in the Indian electricity market.

The basic “Demand Forecasting” objective from the perspective of DSM is that the benefit of the electricity services should be measured in terms of energy services provided, and not in terms of energy sold. Thus to assess the DSM potential, scenario based forecasting approach is most suitable. Where the growth in energy services is forecasted with and without energy efficient services.

STEP 2: Demand Forecasting Methods

Different methods are developed for forecasting the demand in the last two decades. In the past, straight line extrapolations of the historical trends served the purpose well. However, with the beginning of rapidly rising energy prices, inflation, appearance of alternative fuels and DSM options, changes in the lifestyle and energy consumption pattern etc, it is become important to use the modelling techniques, to capture the effect of factors such as energy efficient technologies, prices, per capita income, population and other variables.

Some of the most commonly used methods for demand forecasting includes econometric regression analysis, appliance saturation methods, end use energy methods, time series / trend analysis etc. The usefulness of each method depends on data availability, customer segmentation, and degree of detail required. An appropriate method could be chosen based on the nature of the data available and the desired nature and level of detail of the forecasts. An approach often used by the forecaster is to employ more than one method and then to compare the forecasts to arrive at a more accurate forecast.

The forecaster may use a combination of forecasting techniques that give him aggregate annual forecasts and those that predict hour-by hour demand for electricity in individual sectors. Central Electricity Authority (CEA) has been using partial end-use method specific to different consumer categories to project demand in different states. The time horizons in CEA’s case have been detail forecast for 5 years which has been extrapolated for the next 5 years. Forecasts done by power survey office of CEA are published as “Electric Power Survey”(EPS).

The methods commonly referred by utilities for demand forecasting are discussed in detail in following sections. Most of these could be used for both long-term as well as short-term forecasting.

Method 1 : Econometric Regression Analysis

Econometric regression analysis uses historical annual energy and economic data to determine customer elasticities. Elasticity is measure of how a customer will change a purchasing pattern in response to a change in price, convenience, reliability and other factors. Based on customer elasticities and assuming that that these elasticities do not change through time, demand forecast is made.

Econometric analysis combines economic theory with statistical methods to produce a system of equations for forecasting energy demand. In this method of forecasting the functional relationships are established between the dependent variable, in this case the electricity demand (energy as well as peak) and explanatory variables like GDP, energy price, energy efficient technologies etc. The relationship is of the form,

ED=f(Y, Pi, Pj, Pop, T)

Where,

ED = Energy demand,
Y = Output or income,
Pi = Power Price,
Pj = Price of related fuels,
Pop = Population and
T = Technology.

The forecast of the explanatory variables is used in the functional relationship to obtain the forecast of the dependent variable i.e. the energy demand. The advantage of this method is that socio economic factors like population, income, tariffs, technology diffusion etc. taken in to account while forecasting the values of the dependent variable. The disadvantage is that econometric methods require consistent data for a reasonably long period of time.

Method 2 : Appliance Saturation Method / End Use Energy Method

The appliance saturation method is an “engineering” type methodology. Load research surveys are made to determine the number of customers with a certain appliance (for example, a refrigerator) and the typical annual energy used by the appliance. Then, on the basis of a forecast of the number of appliances expected in the future, together with the forecast of how the annual energy usage per appliance will change, the energy demand forecast is made.

The end use energy method is similar to the appliance saturation method, except that instead of using an appliance as forecasting basis, the basis is the end use process. The floor space and kilowatt-hour energy consumption of the principle electric devices per square foot (space heating and cooling, lighting and auxiliaries) is determined on the basis of load research survey. Based on a forecast of the floor space, the energy sales forecast is developed.

The end use approach attempts to capture the impact of energy usage patterns of various devices and systems. This method focuses on the end use of electricity. The energy usage by various appliances and devices is calculated using the following relationship.

E= S * N * P * H

Where,
E = Energy Consumption of an appliance (kWh),
S = Number of appliances per consumer,
N = Number of consumers,
P = Power requirement of the appliance (kW),
H = Hours of appliance usage (Hours).

The consumption of different end uses is summed up to arrive at the total consumption in a particular sector. The appliance ownership and usage is determined for different categories and classes of consumers. Advantage of this method is that the socio-economic factors can be taken in to account.

Method 3 : Time Series Analysis

This method of forecasting future values of a variable involves the fitting of a trend line to the historical data of a certain variable, using a method of least squares. The method uses auto regression i.e. previous values of a variable are used to predict the future values. The function used is as follows,

Where,

Yt = is the value of the variable under study,

t = time period, and

(Y(t-1),Y( t-2), Y(t-3) , …….. Y(t-k) ) are previous values of the variable.

The advantage of this method is its simplicity and ease of collection data as the historical data for the dependent variable only, is required. The disadvantage is that method does not describe the cause and effect relationship. Thus the time series does not provide an insight in to the changes occurring in the variable under study. This method is suitable for short term forecasts.

Method 4 : Trend Analysis

This method falls under the category of the non-causal models of demand forecasting that do not explain how the values of the variable being projected and determined. Here, we express the variable to be predicted purely as a function of time, rather than by relating it to other economic, demographic, policy and technology variables. This function of time is obtained as the function that best explains the available data, and is observed to be most suitable for short term projections.

Forecasting of demand using this method is popular due to the ease of use and simplicity of the method. Historical data for the variable under study is collected and the same pattern is assumed to continue in future. The disadvantage of this method is that economic and demographic, energy efficient technology factors are not taken in to account.

STEP 3: Modelling the Demand Forecast

In modelling the demand forecasts, utilities can adopt a two way approach such as energy sales approach as well as generation performance approach. The generation performance approach considers historic unit performance, generation capacity expansion, availability / plant load factor projections etc, it captures only the restricted demand (constrained demand forecast) as well as it dose not give insight of category / end use specific demand. In short the generation performance approach does not quantify the unmet demand due to the load shedding owing to the energy shortage situation within the region. However, energy sales approach could be modified based on load shedding details to take care of unmet demand and to get the unrestricted demand (unconstrained demand forecast) in the region. The energy sales approach is also flexible enough to incorporate the category specific / end use process demand and its sensitivity to the diffusion of energy efficient technologies and DSM options.

The output of any load forecast exercise of most of the utilities includes a forecast of the annual energy sales (in kWh) and the annual peak demand (in KVA or kW). During the forecasting exercise it is worthwhile to forecast the annual energy sales first and then use the forecasted energy sales in determining the annual peak demand forecast after analysing the annual load factor. Since the annual energy sales data is the integration of the hourly loads during the year, it is less prone to weather and other spurious effects. A category / end use process specific forecasting method could be adopted by utilities to forecast the energy consumption. Also, a combination of different forecasting techniques could be used to suit the availability of data and information. The profile of annual and daily demands could be determined based on historic data. The load demands at other periods (seasonal variations and minimum loads) shall be derived based on the annual peak demand and past pattern of load variations.

The best way of modelling the demand from DSM perspective, is based on scenario analysis which compares different combinations of technological option to provide the same level of energy services. The different scenarios could be constructed and differentiated according to the level of projected energy service growth (i.e. high, medium, low) and the degree of implementation of energy efficiency improvements like diffusion of energy efficient technologies, DSM options etc.

The starting point to estimate the potential for various DSM options is to construct at least two end use scenarios, a baseline scenario (scenario without diffusion of energy efficient technologies and DSM options) and an energy efficient scenario which considers improvements in end use efficiencies and possible DSM options. However one can include some efficiency improvements in baseline scenario as well based on the assumption that some efficiency improvement will happen naturally without any specific market intervention to stimulate such improvements. The scenario which dose not considers the improvement in end use energy efficiency at all and thus maintaining the current levels of energy efficiency is called as “Frozen Efficiency Scenario”.

The energy efficient scenario can be derived from the set of efficiency improvements / DSM options in several end uses and sectors or for one end use measure. This efficient scenario can consider the possible technical improvements in equipments, processes and buildings that can be introduced in the projected year. However potential for DSM / energy efficiency improvement can be characterised in different ways, one way is to estimate the hypothetical savings that could be achieved if all the systems could be converted instantaneously, whereas other way is to estimate the savings that could be achieved if retiring systems were replaced with more efficient one. To asses the diffusion of energy efficient technologies in place of retiring system various technology forecasting tools could be used.

In modelling the demand forecast, it is always advisable that discrete scenarios such as high growth, moderate growth and low growth must be constructed to examine the impact of variation of the key drivers and to assess the possible range of demand

STEP 4: Reviewing the Results

In electric utilities there are a number of factors that drive the forecast and the forecast further drives the various plans and decisions on investment, construction and conservation.

With the onset of inflation and rapidly rising energy prices, emergence of alternative fuels and efficient technologies (in energy supply and end-use), changes in lifestyles, institutional changes etc. To deal with all of the above many forecasting techniques have been developed, ranging from very simple extrapolation methods to more complex time series techniques, for purposes of prediction, which capture the effect of factors such as prices, income, population, technology and other economic, demographic, policy and technological variables.

Though forecasting tools are available to aid in the process, the key contributors in the process are the planning personnel with their knowledge about the electricity consumers and an understanding of the way they use electricity. The demand for electricity is dependent on the magnitude and growth of the economy. Robust economic growth creates more jobs, which leads to increased population in service territory, which, in turn leads to consumers who use more electricity. Hence, demand forecast is not a one time exercise but needs to be constantly monitored against actual and updated for any major development or change in other external demand drivers like policies, industry growth, changes in specific industry segments etc.


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