Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques

Shawon, Shah Mohammad Rezwanul Haque; Saaklayen, Abu

doi:10.1109/epec52095.2021.9621686

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…A comparative study on conventional statistical models namely autoregressive mean average and autoregressive integrated moving average with that of support vector machine learning methods was conducted. The results proved that the machine learning methods produce better forecasting results than those of conventional methods [22]. Table 1 shows the summary of physical, statistical, and numerical methods used for wind prediction.…”

Section: Statistical Numerical Methodsmentioning

confidence: 92%

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

Sri Preethaa,

Muthuramalingam,

Natarajan

et al. 2023

Sustainability

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

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Section: Statistical Numerical Methodsmentioning

confidence: 92%

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

Sri Preethaa,

Muthuramalingam,

Natarajan

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

Mollick,

Hashmi,

Sabuj

2024

Sustainable Energy res.

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The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques

Cited by 3 publications

References 33 publications

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

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

Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

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