Rural electric power requirements forecasts: detecting and correcting for weaknesses and bias.

• Author: Murry, Donald A.

Based on preparing forecasts for Power Requirements Studies (PRS) and reviewing others, we have observed two re-occurring statistical weaknesses in forecasts of small, rural systems.

The first weakness is an over-reliance on trending, even though the trends are often hidden in econometric equations. The second is a significant statistical bias that goes unreported and undetected. Since both affect the policy decisions of system needs, they reach beyond the arcane world of econometrics. That is, they can lead to very costly mistakes, even affecting a system's ability to compete. And even when detected, if uncorrected, they leave managers with unappealing options. If managers follow their PRS forecasts, they may reach erroneous conclusions about need. If they ignore their PRS's, they have little more than intuition as a guide. Fortunately, these problems can often be corrected, at least partially. In this paper we have used the actual consumption and economic data of a cooperative distribution system to illustrate these problems and their effects.

Trending

In rural electrical systems, forecasting the determination of factors that influence the level of peak usage and load almost inevitably centers on the availability of data. The economic, demographic, and even weather data for the rural areas are limited, more so than for most metropolitan areas. That limitation often leads to selecting data elements for a forecast because they are available rather than for their theoretical relationship to electric consumption. Consequently, the equations used to forecast kilowatt and kilowatt hour (kWh) requirements often are conceptually weak. (In econometrics this is known as weak model specification).

As a substitute for a more thoroughly defined system, analysts often use a trend variable for time to forecast prospective growth. That fails to explain the underlying causes of growth in consumption. Even when the forecasts conform to realistic growth expectations and appear reasonable, models fail to explain explicitly the underlying causes of consumption. These models provide little insight into economic and demographic factors that affect consumption, which leads to two difficulties. First, the models will miss significant structural changes, and second, the models provide no means to assess program effects.

Structural changes will be masked by over-reliance on trending forecasts, whether or not the trend is developed econometrically. Unforeseen...