Application of machine learning for wind energy from design to energy-Water nexus: A Survey

Elyasichamazkoti, Farhad; Khajehpoor, Abolhasan

doi:10.1016/j.nexus.2021.100011

Cited by 30 publications

(11 citation statements)

References 75 publications

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“…This study provides a complete analysis of machine learning in Wind energy systems, examining the most widely used research in various situations and concluding that artificial neural networks might be a more sustainable strategy in many circumstances than traditional approaches [116]. Since 2015, a significant number of research articles on this issue have been published.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

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Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Wind energy A hybrid machine learning method for treatment and monitoring of ocean Wind power. 24 [116] Wind energy A novel machine learning for predicting the Wind power parameters.…”

Section: [115]mentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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“…The signals are added and gathered by the cell body. The axon uses a lengthy fiber to transmit the signal from the cell body to the other neurons [35]. A synapse is formed when a cell's axon connects to a cell's dendrite.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…A training procedure is carried out when these connection weights are adjusted orderly and using an appropriate learning technique. The machine learning technique presents the input to the network and the desired result, then changes the consequences so that the network can generate the desired output [35]. The weights will have important information after training, but it will be redundant and worthless before training.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Venkatesan

Lim

et al. 2022

Electronics

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Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

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“…54 While previous studies have explored surrogate models using methods like polynomial-chaos expansion and Kriging, 55 there is a growing interest in utilizing machine learning approaches. However, according to Elyasichamazkoti and Khajehpoor, 56 most existing studies develop surrogate models focusing on specific areas such as fault detection, wind speed and power forecasting, power optimization, and control. Limited research has been conducted on creating surrogate models that accurately capture the dynamic response of offshore wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling

Guth,

Katsidoniotaki,

Sapsis

2023

Wind Energy

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Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods.

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Application of machine learning for wind energy from design to energy-Water nexus: A Survey

Cited by 30 publications

References 75 publications

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling

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