Incorporating sources of uncertainty in forecasting peak power loads-a Monte Carlo analysis using the extreme value distribution

The extreme value distribution (EVD), in conjunction with Monte Carlo simulations, is used to analyze sources of uncertainty in forecasting annual peak power loads. The methodology is applied to 1984-1986 load and weather data for a public utility district near Spokane, Washington. The methodology embodies a four-step approach: estimate a weather-sensitive daily peak load model, simulate historical peak loads, estimate the parameters of an extreme value distribution, and predict the probability points associated with different forecast horizons. Monte Carlo analysis is used to incorporate the uncertainty of the disturbances in the estimated daily load model. A separate EVD is estimated for each Monte Carlo simulation, and then the estimated EVDs are used to derive a composite distribution. Corrections are made for the small couple bias in maximum-likelihood estimates of the EVD parameters. A bootstrapping technique extends the procedure to examine the uncertainty of the daily load model´s structural parameters