A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

Sustainability, Journal Year: 2023, Volume and Issue: 15(17), P. 12914 - 12914

Published: Aug. 26, 2023

The wind is a crucial factor in various domains such as weather forecasting, the power industry, agriculture, structural health monitoring, and so on. variability unpredictable nature of challenge faced by most wind-energy-based sectors. Several atmospheric geographical factors influence characteristics. Many forecasting methods tools have been introduced since early times. Wind can be carried out short-, medium-, long-term. uncertainty accuracy techniques. This article brings general background physical, statistical, intelligent approaches their used to predict characteristics challenges—this work’s objective improve effective data-driven models for wind-power production. investigation listing effectiveness improved machine learning estimate univariate wind-energy time-based data crucially prominent focus this work. performance ML predicting was examined using ensemble (ES) models, boosted trees bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks recently constructed speed due artificial intelligence (AI) advancement. Based on model summary, further directions research application developments planned.

Language: Английский

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Energy enhancement through noise minimization using acoustic metamaterials in a wind farm

Renewable Energy, Journal Year: 2024, Volume and Issue: 224, P. 120188 - 120188

Published: Feb. 19, 2024

The determinantal noise pollution from wind turbines has constricted the acceptance of energy, posing health and environmental concerns. Existing solutions often compromise turbine efficiency or farm output, limiting full utilization energy. To address this challenge, we propose a novel method effectively employing acoustic metamaterials (AMMs) inside that leverages phase cancellation for suppression energy enhancement. Through combined retrieval, propagation model, genetic algorithm, determine optimal layout structural design these AMMs to minimize noise. We present two AMM designs, full-walled segmented, demonstrate their effectiveness both single-turbine multiple-turbine scenarios by achieving 91% (in Pa) 10%–68% reduction, respectively, compared reference layouts. Furthermore, exhibit AMM's impact in enhancing throughput farms installing an additional noise-restricted area existing AMM-equipped while maintaining 70% reduction levels. This approach paves way constructing near urban-suburban areas, complying with landscaping visual government policies, securing community towards sustainable power generation systems.

Language: Английский

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Multistep short‐term wind speed prediction with rank pooling and fast Fourier transformation

Wind Energy, Journal Year: 2024, Volume and Issue: 27(7), P. 667 - 694

Published: May 13, 2024

Abstract Short‐term wind speed prediction is essential for economical power utilization. The real‐world data are typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model multistep prediction, namely, LR‐FFT‐RP‐MLP/LSTM (linear fast Fourier transform rank pooling multiple‐layer perceptron/long short‐term memory). Our processes the local global input features simultaneously. We leverage RP feature extraction capture temporal structure while maintaining order. Besides, understand periodic patterns, exploit FFT extract relevant frequency components in data. resulting are, respectively, integrated with original fed into an MLP/LSTM layer initial predictions. Finally, linear regression collaborate these predictions produce final prediction. proposed evaluated using real collected from 2010 2020, demonstrating superior forecasting capabilities when compared state‐of‐the‐art single Overall, study presents promising approach improving accuracy of forecasting.

Language: Английский

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Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100475 - 100475

Published: Jan. 1, 2025

Language: Английский

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Determination of the performance of training algorithms and activation functions in meteorological drought index prediction with nonlinear autoregressive neural network

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 20, 2025

Language: Английский

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Forecasting techniques for power systems with renewables

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 381 - 412

Published: Jan. 1, 2025

Language: Английский

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Computer-aided many-objective optimization framework via deep learning surrogate models: Promoting carbon reduction in refining processes from a life cycle perspective

Chemical Engineering Science, Journal Year: 2025, Volume and Issue: unknown, P. 121350 - 121350

Published: Feb. 1, 2025

Language: Английский

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Integrating signal pairing evaluation metrics with deep learning for wind power forecasting through coupled multiple modal decomposition and aggregation

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113394 - 113394

Published: April 1, 2025

Language: Английский

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Introduction to Wind Farm Micro-siting

Published: Jan. 1, 2025

Language: Английский

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Maximizing Energy Production from Wind Farm Layout Using Model Predictive Control

Published: Jan. 1, 2025

Language: Английский

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