Load and Price Forecasting via Wavelet Transform and Neural Networks

Short-term load forecasting (STLF) plays an important role in the operational planning and the security functions of an energy management system and various forecasting techniques have been proposed and applied to power systems.

Abstract

Short-term load forecasting (STLF) plays an important role in the operational planning and the security functions of an energy management system. The STLF is aimed at predicting electric loads for a period of minutes, hours, days or weeks for the purpose of providing fundamental load profiles to the system. Over the years, considerable research effort has been devoted to STLF and various forecasting techniques have been proposed and applied to power systems. Conventional methods based on time series analysis exploit the inherent relationship between the present hour load, weather variables and the past hour load. Autoregressive (AR), moving average (MA) and mixed autoregressive and moving average (ARMA) models are prominent in the time series approach. The main disadvantage is that these models require complex modelling techniques and heavy computational effort to produce reasonably accurate results [1].