Non-parametric short-term load forecasting

Abstract

Load forecasting is an important problem in the operation and planning of electrical power generation, as well as in transmission and distribution networks. This paper is interested by short-term load forecasting. It deals with the development of a reliable and efficient Kernel regression model to forecast the load in the Hydro Québec distribution network. A set of past load history comprising of weather information and load consumption is used. A non-parametric model serves to establish a relationship among past, current and future temperatures and the system loads. The paper proposes a class of flexible conditional probability models and techniques for classification and regression problems. A group of regression models is used, each one focusing on consumer classes characterising specific load behaviour. Each forecasting process has the information of the past 300 h and yields estimated loads for next 120 h. Numerical investigations show that the suggested technique is an efficient way of computing forecast statistics.