An Effective Wind Power Prediction using Latent Regression Models

Bouyeddou, Benamar; Harrou, Fouzi; Saidi, Ahmed; Sun, Ying

doi:10.1109/iciss53185.2021.9533242

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…The vertical path symbolizes the one-way flow from the input layer to the hidden layer and subsequently to the output layer. At the same time, the horizontal direction concurrently calculates the forward LSTM hidden vector (h t ) and the reverse LSTM hidden vector h t for each time step t. To derive the Bi-LSTM model's final prediction result, the authors here employ an approach based on linking two hidden states, as represented by Equations ( 7)- (9).…”

Section: Bidirectional Lstmmentioning

confidence: 99%

“…However, designing a physical model may be time-consuming and expensive, leading to subpar forecast accuracy at the regional scale [8]. Functional dependencies are derived directly from the data to construct a model that represents the relationships between wind speed and other input variables [9,10], as opposed to physical techniques based on relatively complicated differential equations. Integrating wind turbines into smart-grids and optimizing the control of electricity production relies heavily on accurate predictions of wind speed.…”

Section: Introductionmentioning

confidence: 99%

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Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

et al. 2023

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Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.

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Section: Bidirectional Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

et al. 2023

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“…Keywords: WPP, energy efficiency, erratic behavior, prediction, MP-CDBN Exploring the Intersection of Social Science and Humanities: Current Research and Challenges spatiotemporal connections among the conditions at neighboring weather stations and the particular construction location, the methods of support vector regression and artificial neural networks have been used. The effectiveness of two widely used latent factor analysis approaches, "principal component regression" (PCR) and "partial least squares regression" (PLSR), for forecasting wind power was examined (Bouyeddou et al 2021). The accuracy of the predicted values produced by the researched strategies is demonstrated using genuine measurements taken each ten minutes from a real turbine.…”

Section: Introductionmentioning

confidence: 99%

Improving wind energy efficiency with machine learning-driven wind speed forecasting

Kumar,

Simalti,

Deshpande

2023

Multidiscip. Sci. J.

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The broad use of wind power plants is a result of the rising need for renewable energy. However, it is difficult to effectively harness wind energy due to the inconsistent and erratic behavior of the wind. Improved wind energy system effectiveness depends on reliable wind speed forecasting. We suggest a unique marine predator-optimized convolutional deep belief network (MP-CDBN) in this study for predicting wind speed. The MPO technique is employed for optimizing the MP-CDBN framework once it has been trained using prior wind data. An evaluation and comparison of the suggested model with other wind speed prediction techniques are conducted. The suggested MP-CDBN model's precise wind speed predictions have the potential to increase the effectiveness of wind energy installations. The suggested approach can aid in lowering carbon dioxide emissions and encouraging the production of renewable energy by increasing the effectiveness of wind power plants.

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“…The data-based statistical models immediately generate functional dependencies from the data to construct a model that describes the links between wind power and other input variables (Bouyeddou et al, 2021), in contrast to the physical techniques based on relatively complex differential equations. Several statistical models, including the autoregressive (AR) model, moving average (MA) model, autoregressive moving average (ARMA) model, and autoregressive integrated moving average (ARIMA) model, provide prediction value as a function of historical wind power (Eissa et al, 2018;Eid et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Saeed¹,

Ibrahim²,

El-kenawy³

et al. 2023

Front. Energy Res.

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Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting.

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An Effective Wind Power Prediction using Latent Regression Models

Cited by 7 publications

References 34 publications

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm

Improving wind energy efficiency with machine learning-driven wind speed forecasting

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

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