Percorrer por autor "Neves, L.P."
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- Assessing the relevance of load profiling information in electrical load forecasting based on neural network modelsPublication . Sousa, J.C.; Neves, L.P.; H.M. JorgeThe 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.
- Forecasting the next day load profile using load profiling information and meteorological variablesPublication . Sousa, J. C.; Jorge, H. M.; Neves, L.P.The article proposes a new approach to support the process of forecasting the hourly electric load values for the following day. The adopted methodology based on neural networks is only supported by detailed information related with consumers' typical behavior and climatic information. The case study was tested in two real distribution substation outputs, demonstrating its effectiveness and practical applicability.