Assessing the relevance of load profiling information in electrical load forecasting based on neural network models

The article is focused on evaluating the relevance of load profiling information in electrical load forecasting, using neural networks as the forecasting methodology. Different models, with and without load profiling information, were tested and compared, and, the importance of the different inputs was investigated, using the concept of partial derivatives to understand the relevance of including this type of data in the input space. The paper presents a model for the day ahead load profile prediction for an area with many consumers. The results were analyzed with a simulated load diagram (to illustrate a distribution feeder) and also with a specific output of a 60/15 kV real distribution substation that feeds a small town. The adopted methodology was successfully implemented and resulted in reducing the mean absolute percentage error between 0.5% and 16%, depending on the nature of the concurrent methodology used and the forecasted day, with a major benefit regarding the treatment of special days (holidays). The results illustrate an interesting potential for the use of the load profiling information in forecasting.

► We integrate information derived from load profiling in load forecasting models. ► Different ANN models, with and without load profiling, are tested and compared. ► The relevance of load profiles is evaluated through a sensitivity analysis in ANN. ► The methodology was evaluated with two different case studies. ► The adopted methodology was successfully validated, especially during holidays.