Input data analysis for optimized short term load forecasts

Accurate electrical short term load forecasts play an important role for grid operation, power plant scheduling and power trading. The need for precise forecasts rises as energy markets are in a phase of transition due to severe changes in the energy system for European countries. This transition is mainly caused by rising shares of renewable energies, increasing energy efficiency and consumption pattern changes. This paper presents a case study based on publicly available data, on how selection of weather and economic data, the historic availability of training data and regional aspects affect the quality of short term load forecasts. By using evaluation metrics, such as the normalized rooted mean square error (NRMSE) and the mean average percentage error (MAPE), forecasting results may be compared to other case studies. Furthermore, the case study is based on a novel model framework utilizing e. g. artificial neuronal network (ANN), support vector regression (SVR) and similar day models. By selecting optimized training data sets we increase forecasting accuracy for supported models up to 11%. Forecasting results of supported models for a whole year, including holidays and weekends, range from 2.1% to 2.38% MAPE.