Assessment of Some Methods for Short-Term Load Forecasting

Accurate forecasting of loads is essential for smart grids and energy markets. This paper compares the performance of the following models in short-term load forecasting: 1) smart metering data based profile models, 2) a neural network (NN) model, and 3) a Kalman-filter based predictor with input nonlinearities and a physically based main structure. The comparison helps method selection for the development of hybrid models for forecasting the load control responses. According to the results all these three modeling approaches show much better performance than 4) the traditional load profiles and 5) a static outdoor temperature dependency model applied with a lag. The neural network model was the most accurate in the comparison, but the differences of the three methods developed were rather small and also other aspects and other methods must be considered and compared when selecting the method for a specific purpose.