Unorganized neural networks applied to streamflow forecasting of Passo Real hydroelectric plant

• Published in: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI) pp. 1 - 6
• Main Authors: Valadares Siqueira, Hugo, Luna, Ivette, Boccato, Levy, Lyra, Christiano
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2016
• Subjects: Biological neural networks; Computer architecture; Echo State Networks; Extreme Learning Machines; Forecasting; Mathematical model; Monthly Seasonal Streamflow Series Forecasting; Neurons; Periodic Autoregressive model; Reservoirs; Training
• DOI: 10.1109/LA-CCI.2016.7885700

Unorganized neural networks - or unorganized machines - are recent developed architectures in the field of computational intelligence, in which the supervised tuning of the free parameters is restricted to the weights of the output layer, by means of a linear least square solution. The remaining weights are randomly generated and stand untrained which become the adjustment process simple and fast. In this work, we considered the Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) to predict the monthly seasonal streamflow series associated to Passo Real hydroelectric plant, located in Brazil. In view of to establish a performance comparison, a periodic autoregressive model was developed. The computational results obtained show the relevance of the proposed networks to solve the problem, extending the possibility of application of the unorganized networks.