The Effect of Driver Variables on the Estimation of Bivariate Probability Density of Peak Loads in Long-Term Horizon

Abstract

It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.

Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method

“…Then, they used Gaussian mixture model to fit the probability distribution function (PDF) of forecasting errors. Reference [15] developed the method proposed by [16] for long-term peak load forecasting considering different driver variables. They estimated the PDF of peak load in long-term horizons considering the most important drivers.…”

“…They estimated the PDF of peak load in long-term horizons considering the most important drivers. Reference [15] conducted a SOMN to estimate the PDF; they concluded their method provides much more accurate estimations with a rapid convergence.…”