Load forecasting techniques for power systems with high levels of unmetered renewable generation: A comparative study

Abstract

Load forecasting remains a challenging problem in power system operation due to growth in low carbon technologies and distributed small scale renewable generation. In this paper we provide a comparative evaluation of a number of linear and non-linear (machine learning) load forecasting models for day-ahead load forecasting under these new conditions. Both autoregressive and exogenous input only models are considered with regressors determined either empirically or by a greedy forward selection methodology. Using data from the Northern Ireland power system as a case study, we show that non-linear models yield significant performance improvements for exogenous input (EI) based models, but that linear models remain competitive for same day last week (SDLW) models that include a historical load term as a regressor.