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Forecasting for Electricity orecas Demand Forecasting for Electricity
Prediction is a very difficult art, especially when it involves the future -Neils Bohr (Nobel Laureate Physicist)

Introduction Introduction
Forecasting demand is both a science and an art. Econometric methods of forecasting, in the context of energy demand forecasting, can be described as ‘the science and art ofspecification, estimation, testing and evaluation of models of economic processes’ that drive the demand for fuels. The need and relevance of forecasting demand for an electric utility has become a much-discussed issue in the recent past. This has led to the development of various new tools and methods for forecasting in the last two decades. In the past, straightline extrapolations of historical energyconsumption trends served well. However, with the onset of inflation and rapidly rising energy prices, emergence of alternative fuels and technologies (in energy supply and end-use), changes in lifestyles, institutional changes etc, it has become imperative to use modeling techniques which capture the effect of factors such as prices, income, population, technology and other economic, demographic,policy and technological variables.

for predictions Need for good predictions
There is an urgent need for precision in the demand forecasts. In the past, the world over, an underestimate was usually attended to by setting up turbine generator plants fired by cheap oil or gas, since they could be set up in a short period of time with relatively small investment. On the other hand, overestimateswere corrected by demand growth. The underlying notion here was that in the worst case, there would be an excess capacity, which would be absorbed soon. In the Indian context, the demands were usually overestimated, notwithstanding which, the capacities fell short of the actual demands on a year to year basis. The presence of economies of scale, lesser focus on environmental concerns,predictability of regulation and a favorable public image, all made the process of forecasting demand much simpler. In contrast, today an underestimate could lead to undercapacity, which would result in poor quality of service including localized brownouts, or even blackouts. An overestimate could lead to the authorization of a plant that may not be needed for several years. Many utilities

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do not earn enough to be able to cover such a cost without offsetting revenues. Moreover, in view of the ongoing reform process, with associated unbundling of electricity supply services, tariff reforms and rising role of the private sector, a realistic assessment of demand assumes ever-greater importance. These are required not merely for ensuring optimal phasing ofinvestments, a long term consideration, but also rationalizing pricing structures and designing demand side management programs, which are in the nature of short- or medium-term needs. The gestation period for power plants, which are set up to meet consumer demand, typically varies between 7 to12 years in the case of thermal and hydro plants and 3 to 5 years for gas-based plants. As a result,utilities must forecast demand for the long run (10 to 20 years), make plans to construct facilities and begin development well before the indices of forecast growth reverse or slowdown. In manufacturing institutions and electric utilities there are a number of factors that drive the forecast, including market share. The forecast further drives various plans and decisions on investment, construction andconservation. Since electric utilities are basically dedicated to the objective of serving consumer demands, in general the consumer can place a reasonable demand on the system in terms of quantity of power. With some built-in reserve capacity, the utilities may have to configure a system to respond to these to the extent possible. In the process of making predictions, forecaster bears in mind...