Operational research has made meaningful contributions to practical forecasting in organizations. An area of substantial activity has been in nonlinear modeling. Based on Particle Swarm Optimization, we discuss a nonlinear method where a self-adaptive approach, named as Particle Swarm Optimization with aging and weakening factors, was applied to training a Focused Time Delay Neural Network. Three freely available benchmark datasets were used to demonstrate the features of the proposed approach compared to the Backpropagation algorithm, Differential Evolution and the Particle Swarm Optimization method when applied for training the artificial neural network. Even acknowledging that the effort in comparing methods across multiple empirical datasets is certainly substantial, the proposed algorithm was used to produce 30 min, 1, 3 and 6 h ahead predictions of wind speed at one site in Brazil. The use of the proposed algorithm goes further than only training the artificial neural network, but also searching the best number of hidden neurons and number of lags. The results have shown that the modified Particle Swarm Optimization algorithm obtained better results in all predictions horizons, and the use of it has remarkably reduced the training time.
Wind power has contributed significantly to the increase in electricity generation, but a decision-making tool capable of dealing with its variability and limited predictability is necessary. For this purpose, a novel self-adaptive approach for kernel recursive least-squares machines named multiple challengers is introduced in this work, which is successfully used to produce very short-term wind power forecasts at eight wind farms in Australia. The proposed method is based on a competitive tracking method, and the algorithm deals with some common difficulties of kernel methods, e.g., the increasing kernel matrix size associated with time and memory complexities and the overfitting problem. The proposed method always considers the new information received by the model, thus identifying changes in the time series, avoiding abrupt loss of information and maintaining a controlled number of examples since there is an adaptive selection of the active kernel. It works with the smallest dictionary possible, reducing the probability of overfitting. Five minute-ahead wind power forecasts are produced and evaluated in terms of point forecast skill scores and calibration. The results of the proposed method are compared with those provided by other kernel-based versions of the recursive least-squares algorithm, an online version of the extreme learning machine method, and the persistence time series model. An increase in the number of kernels used in the ensemble system can lead to better results when compared with those of single-kernel models.INDEX TERMS Multiple kernel learning, online training, renewable energy, wind power forecasting.
Este trabalho utiliza o método de Otimização por Nuvens de Partículas (Particle Swarm Optimization-PSO) para estruturar Redes Neurais Artificiais (RNAs) que estimam a velocidade do vento em parques eólicos. As RNAs empregadas são do tipo NARX e FTDNN. Os resultados obtidos com estas RNAs são comparados com os obtidos com o modelo de Persistência, muito utilizado na prática. Foi utilizada uma série histórica com 45.658 dados de medição da velocidade do vento, em que 80% das medições foram selecionadas para a fase de treinamento e 20% para a validação. Como critérios de avaliação do desempenho das redes foram considerados as seguintes medidas de erro: MAE, RMSE e MAPE. Os resultados das previsões entre 1h e 6h são muito similares para todos os modelos, entretanto nas previsões para 12h e 48h existem diferenças entre os valores do MAPE que apontam a rede NARX como de maior aderência na projeção dos dados considerados.
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