Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

João Fausto L. de Oliveira,Attilio Converti,Paulo S.G. de Mattos Neto,Jônatas Belotti,Hugo Siqueira,Marcos de Almeida Leone Filho,Lilian Araujo,Fábio Usberti,Leonie Asfora Sarubbo,Manoel H. N. Marinho,Sérgio L. Stevan

doi:10.3390/en13184769

Abstract

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

Highlights

Planning the operation of a power generation system is defined by establishing the use of energy sources in the most efficient way [1,2,3]
Regarding the feedforward neural models, one can observe for 16 of 20 cases (80%), the extreme learning machines (ELM) overcame the traditional multilayer perceptron (MLP) and Radial Basis Function Network (RBF) architectures
This work investigated the performance of unorganized machines—extreme learning machines (ELM), echo state networks (ESN), and ELM-based ensembles—on monthly seasonal streamflow series forecasting from hydroelectric plants

Summary

Introduction

Planning the operation of a power generation system is defined by establishing the use of energy sources in the most efficient way [1,2,3]. Good predictions of river streamflows allow resource management according to their future availability [4]. This is mandatory for countries where there are hydroelectric plants [5,6,7]. The International Hydropower Association published the Hydropower Status Report 2020 [8], showing that 4306 TWh of electricity was generated in the world using hydroelectric plants in 2019. This amount represents the single most significant contribution from a renewable energy source in history. The top countries in hydropower installed capacity are China (356.40 GW), Brazil (109.06 GW), United States (102.75 GW), and Canada (81.39 GW)

Methods

Results

Conclusion