Esnpredictor: Ferramenta De Previsão De Séries Temporais Baseada Em Echo State Networks Otimizadas Por Algoritmos Genéticos E Particle Swarm Optimization

RUEDA, CAMILO VELASCO

doi:10.17771/pucrio.acad.24785

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Also, ESNs are one of the most well-known types of recurrent neural networks because of their excellent performance when nonlinear dynamic system modeling. It is capable to learn the complex nonlinear behaviors of dynamical systems and these have been successfully applied to a wide range of engineering problems, including time series forecasting [49][50][51] and load forecasting [30,[52][53][54][55][56] problems.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

et al. 2020

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Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tuning ESN to solve real-world forecasting problems, as well as the evaluation of the performance of Bayesian optimization with different acquisition function settings. For this purpose, the ESN hyperparameters were set as variables to be optimized. Then, the adopted BOA employs a probabilist model using Gaussian process to find the best set of ESN hyperparameters using three different options of acquisition function and a surrogate utility function. Finally, the optimized hyperparameters are used by the ESN for predictions. Two datasets have been used to test the effectiveness of the proposed forecasting ESN model using BOA approaches, one from Poland and another from Brazil. The results of optimization statistics, convergence curves, execution time profile, and the hyperparameters’ best solution frequencies indicate that each problem requires a different setting for the BOA. Simulation results are promising in terms of short-term load forecasting quality and low error predictions may be achieved, given the correct options settings are used. Furthermore, since there is not an optimal global optimization solution known for real-world problems, correlations among certain values of hyperparameters are useful to guide the selection of such a solution.

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Section: Introductionmentioning

confidence: 99%

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

et al. 2020

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“…A utilização de Algoritmos Genéticos para a otimização de parâmetros globais na configuração da rede ESN foi empregada em trabalhos anteriores (Xu, 2005), (Ferreira, 2011), (Velasco, 2014), com resultados bastante satisfatórios.…”

Section: Modelo Híbrido Echo State Network-genetic Algorithm-separati...unclassified

“…A representação é fundamental na modelagem de um Algoritmo Genético, pois nesta etapa é definida a estrutura do cromossomo, com os respectivos genes que o compõem, de maneira que este seja capaz de descrever todas as variáveis e condições do problema dentro do espaço de busca (Velasco, 2014). Tem-se diferentes tipos de representação e cada uma é utilizada em função do problema abordado.…”

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Modelo Híbrido Esn-Ga-Srg: Uma Abordagem De Otimização E Seleção De Topologias Em Echo State Networks Para Previsão De Séries Temporais

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Esnpredictor: Ferramenta De Previsão De Séries Temporais Baseada Em Echo State Networks Otimizadas Por Algoritmos Genéticos E Particle Swarm Optimization

Cited by 2 publications

References 19 publications

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting

Modelo Híbrido Esn-Ga-Srg: Uma Abordagem De Otimização E Seleção De Topologias Em Echo State Networks Para Previsão De Séries Temporais

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