A Bottom-up Method for Probabilistic Short-Term Load Forecasting Based on Medium Voltage Load Patterns

Abstract

Load forecasting has always been an essential part of power system planning and operation. In recent decades, the competition of the market and the requirements of renewable integration lead more attention to probabilistic load forecasting methods, which can reflect forecasting uncertainties through prediction intervals and hence benefit decision-making activities in system operation. Moreover, with the development of smart grid and power metering techniques, power companies have collected enormous load data about electricity customers and substations. The abundant load data allow us to utilize medium voltage measurement data to achieve better accuracy in high voltage transmission substation load forecasting. In this paper, a bottom-up probabilistic forecasting method is proposed for high voltage transmission substation short-term load forecasting, in which the probability distributions of medium voltage day-ahead load forecasting values are estimated and added up to form high voltage load predictions. Two bottom-up frameworks based on load patterns collected from medium voltage outgoing lines and substations are proposed respectively, in which mismatches between load data at different levels are estimated for correcting high voltage predictions. The comparison of predictions obtained by traditional and bottom-up methods demonstrates that the proposed method obtains high voltage load forecasting more accurately and give narrower prediction intervals.