Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison

Flores, Juan J.; Gonzalez, Jose R. Cedeno; Rodríguez, Héctor; Graff, Mario; Lopez-Farias, Rodrigo; Calderón, Félix

doi:10.3390/en12183545

Cited by 6 publications

(4 citation statements)

References 57 publications

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“…On the other hand, the study conducted by Flores et al (2019) tested various forecasting approaches [including MLP-ANN, Nearest neighbor (NN), Fuzzy forecasting, Evolving Directed Acyclic Graph (EVOdag), and ARIMA] for 1-day-ahead wind speed forecasting. To improve the performance of the AI-based forecasters, the authors accompanied each technique with an evolutionary optimization approach.…”

Section: Evolutionary Optimization Algorithms and Ann-based Forecasting Methodologiesmentioning

confidence: 99%

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

et al. 2021

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The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.

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Section: Evolutionary Optimization Algorithms and Ann-based Forecasting Methodologiesmentioning

confidence: 99%

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

et al. 2021

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“…The accuracy evaluation of OSWS retrievals commonly employs the root mean square error (RMSE) [36,37] as a metric. In addition, mean absolute error (MAE) [37,38] and symmetric mean absolute percentage error (SMAPE) [39,40] were selected as the accuracy metrics. The ERA-5 Reanalysis Wind Speed product provides analyzed 10 m wind speed and wind direction with a resolution of 12.5 km [30], typically considered a reliable reference data source for comparison.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea

Wan,

Liu,

Guo

et al. 2024

JMSE

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This paper designed a Generative Adversarial Network (GAN)-based super-resolution framework for scatterometer ocean surface wind speed (OSWS) mapping. An improved GAN, WSGAN, was well-trained to generate high-resolution OSWS (~1/64 km) from low-resolution OSWS (~12.5 km) retrieved from scatterometer observations. The generator of GAN incorporated Synthetic Aperture Radar (SAR) information in the training phase. Therefore, the pre-trained model could reconstruct high-resolution OSWS with historical local spatial and texture information. The training experiments were executed in the South China Sea using the OSWS generated from the Advanced SCATterometer (ASCAT) scatterometer and Sentinel-1 SAR OSWS set. Several GAN-based methods were compared, and WSGAN performed the best in most sea states, enabling more detail mining with fewer checkerboard artifacts at a scale factor of eight. The model reaches an overall root mean square error (RMSE) of 0.81 m/s and an overall mean absolute error (MAE) of 0.68 m/s in the collocation region of ASCAT and Sentinel-1. The model also exhibits excellent generalization capability in another scatterometer with an overall RMSE of 1.11 m/s. This study benefits high-resolution OSWS users when no SAR observation is available.

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“…However, this GA-ANN technique failed to predict long-term solar power (Jafarian-Namin et al, 2019). NN, MLP-ANN, ARIMA, EVOdag, and Fuzzy forecasting were examined for predicting wind speed 1 day earlier (Flores et al, 2019)…”

Section: Evolutionary Optimization Algorithms and Ann-based Forecasti...mentioning

confidence: 99%

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

Mohammed Saleh Al Ansari

2023

IJRITCC

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Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap.

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Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison

Cited by 6 publications

References 57 publications

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review

Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea

Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research

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