Percorrer por autor "Jorge, Humberto M."
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- Multi-objective optimization using NSGA-II for power distribution system reconfigurationPublication . Vitorino, Romeu M.; Jorge, Humberto M.; Neves, Luís P.This study proposes a new strategy to solve the problem of radial power distribution system (RDS) recon figuration in a multi-objective and constrained environment. Due to the presence of various conflicting objectives and constraints, the proposed strategy uses the Elitist Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), an effective evolutionarymulti-objective optimization technique. NSGA-II determines a set of pareto-optimal solutions for the power distribution system topology, considering power losses, reliability and investment in tie-switches. The methodology adopted to evaluate the RDS reliability uses a non-sequential Monte Carlo Simulation and is focused on the impacts of branch failures for interruption energy assessment. The effectiveness of the proposed methodology is demonstrated on a 69 bus RDS.
- Short-term load forecasting based on support vector regression and load profilingPublication . Sousa, João C.; Jorge, Humberto M.; Neves, Luís P.The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance.