A probabilistic approach to handle uncertainties in load forecasting

摘要

This paper presents a novel method to include the uncertainties of weather variables in neural network based load forecasting models. The methodology is based on the probability distribution of uncertain variables. The proposed method consists of traditionally trained neural networks and a set of equations to calculate the mean forecast and the variance of the forecast. This method was tested for daily forecasts for one year. The test results indicated that in addition to the availability of the confidence interval, the new method also provides a more accurate mean forecast than traditional neural networks alone.