Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction

Bourakadi, Dounia El; Yahyaouy, Ali; Boumhidi, Jaouad

doi:10.1007/s00521-021-06619-x

Cited by 18 publications

(5 citation statements)

References 43 publications

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“…The developed model, designated as R-ELM-GA, can make use of the main features of the R-ELM technique while eliminating the random selection of the hidden node number or the recurrent tests that need more training time and result in slower convergence. Following [33], we can determine the hidden nodes' 𝐿 number in the hidden layer as (16):…”

Section: Proposed Methodsmentioning

confidence: 99%

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.

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Section: Proposed Methodsmentioning

confidence: 99%

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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“…Bourakadi et al. (2022) 185 proposed an AE model to predict short-term wind power using wind speed and power data at 1-h intervals. It is worth mentioning that CNNs are often employed as data pre-processing tools for other models, owing to their excellent feature extraction capability.…”

Section: State-of-the-art Deterministic Forecasting Methodsmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…However, the sequential search strategies are characterized by a major drawback; the order of parameter entry (or deletion) affects the selected model [32]. To overcome this issue, the use of EA-based approaches [33], such as genetic algorithms (GAs) [34], the BDE algorithm [21], particle swarm optimization (PSO) [35], the coral reef optimization (CRO) algorithm [36], or a combination of these techniques, have been shown to be effective even if they are computationally more demanding. In practice, the main advantages of EAs are (i) their fast convergence to a near-global optimum, (ii) their superior global searching capability in complicated search spaces, and (iii) their applicability even when gradient information is not readily achievable.…”

Section: The Motivation For Feature Selectionmentioning

confidence: 99%

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Al-Dahidi,

Baraldi,

Fresc

et al. 2024

Energies

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We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

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Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction

Cited by 18 publications

References 43 publications

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

An overview of deterministic and probabilistic forecasting methods of wind energy

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

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