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.
- Loss and reliability optimization for power distribution system operationPublication . Vitorino, R.M.; Jorge, H.M.; Neves, L.P.This study presents an optimization method that combines simultaneously the reliability and the efficiency of radial power distribution systems (RDS), minimizing active energy losses, through a process of network reconfiguration. The study is based on the failure analysis on network branches, with a special concern regarding the protection system response to faults and the service restoration procedures, during the emergency state. A non-sequential Monte Carlo simulation based on the branch reliability was used to evaluate reliability of the network configurations. Due to a large number of possible configurations and the need of an efficient search, the optimization is made through an improved genetic algorithm (IGA). The method analyses the RDS considering in a first step, the absence of investment, and in a second step, the possibility of placing a limited number of new tie-switches in certain branches, according to the definitions made by a decision maker. The effectiveness of the proposed methodology is demonstrated through the analysis of a 69 bus RDS and by comparison against other reported methodologies.