Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

Siqueira, Hugo Valadares; Belotti, Jônatas Trabuco; Boccato, Levy; Luna, Ivette; Attux, Romis; Filho, Christiano Lyra

doi:10.1111/itor.12908

Cited by 12 publications

(8 citation statements)

References 61 publications

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“…It is clear that the linear ARIMA model did not overcome any machine learning approach. One possibility of enhancing its prediction capability is to use bio-inspired metaheuristics [75]. Regarding the other single models, we observe high variability in terms of the performance or dispersion.…”

Section: Discussionmentioning

confidence: 89%

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

et al. 2022

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Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics.

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Section: Discussionmentioning

confidence: 89%

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

et al. 2022

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“…Regarding the forecasting models, an investigation must be developed to analyze the approaches to deal with multi-step horizons [49]. Also, some models have stood out in the current days, among linear [50], nonlinear [51], and combination proposals [52][53] that can be used considering the perspectives of this work.…”

Section: Discussionmentioning

confidence: 99%

Climate Indices Impact in Monthly Streamflow Series Forecasting

et al. 2023

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“…In this work, 6 variations of the GA are used, all of them based on the premises described as follows [38,41,46]:…”

Section: Methodology For Simulation Analysis 411 Ga Variationsmentioning

confidence: 99%

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

Borges

Guerreiro

Monteiro

et al. 2022

Sensors

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This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID.

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Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

Cited by 12 publications

References 61 publications

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Climate Indices Impact in Monthly Streamflow Series Forecasting

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

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