Abstract
Short-time load forecasting is necessary for making optimal decisions regarding the current and future operational states of a power system. The correctness of those decisions depends on the forecast accuracy. The uncertainty in load forecast can be quantified using the concept of a confidence interval. The most commonly used approximation of a confidence interval is known as the standard interval, which is determined from the past forecast errors. This paper presents an alternative method of confidence interval construction for load forecast. It is based on the modeling load as a multivariable probability density function and finding the load forecast as a conditional distribution of load given the information on the explanatory variables, including any uncertainty in their values. The confidence interval is established using the information on the local load variability for the expected conditions, extracted from historical data by means of nonparametric density estimation. This approach allows to construct a confidence interval based on the expected conditions, known with some degree of uncertainty, rather than on the errors observed in the past. It provides a way of translating any uncertainty in the anticipated weather conditions into the confidence interval for the predicted load.
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