Machine Learning and Statistical Techniques for Daily Wind Energy Prediction

Wickramasinghe, Lasini; Ekanayake, Piyal; Jayasinghe, Jeevani

doi:10.35378/gujs.961338

Cited by 4 publications

(1 citation statement)

References 27 publications

(18 reference statements)

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“…When it comes to real-world AI applications, the breadth of possibilities is striking. AI-driven techniques play a pivotal role in forecasting Turkey's natural gas consumption [10], utilizing LSTM-based deep learning methods for earthquake prediction through ionospheric data analysis [11], and improving the precision of daily wind energy predictions through machine learning and statistical techniques [12]. In the healthcare sector, AI comes to the forefront with a machine learning model for diagnosing Type 2 diabetes based on health behavior [13], while in the field of speech recognition, recurrent units like LSTM and GRU find applications in Turkish speech recognition techniques and broader speech processing endeavors [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Öz,

Özarpa

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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This study uses neural networks to explore the intricate longitudinal progression of decommissioned geostationary satellites. The goal is to model and predict satellites' longitudinal dynamics across time dimensions. Historical satellite longitude data undergoes thorough preprocessing to train time series neural networks in both single-input and 3-input configurations for all six decommissioned satellites, yielding comprehensive longitudinal behavior insights. Results reveal impressive outcomes: average Mean Squared Error (MSE) between predicted and measured longitudes is 1.55x10-3, with regression close to unity. This convergence implies a strong alignment between the neural network methodology employed and the intricate problem domain. These results accentuate the suitability and effectiveness of the chosen neural network approach in addressing the challenges posed by decommissioned geostationary satellite trajectory modeling. The study's implications span various fields. Insight into long-term orbital shifts aids in understanding satellite behaviors, enhancing trajectory predictions and decision-making in satellite management and space technology advancement. Additionally the research emphasizes the importance of accurate predictions about satellite behavior after decommissioning. This contributes to better mission planning, resource optimization, and more efficient strategies for dealing with space debris.

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Section: Introductionmentioning

confidence: 99%

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Öz,

Özarpa

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Evaluation of TerraClimate gridded data in investigating the changes of reference evapotranspiration in different climates of Iran

Solaimani,

Bararkhanpour Ahmadi

2024

Journal of Hydrology: Regional Studies

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Unlocking the potential: A review of artificial intelligence applications in wind energy

Dörterler,

Arslan,

Özdemir

2024

Expert Systems

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This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field.

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Machine Learning and Statistical Techniques for Daily Wind Energy Prediction

Abstract: Highlights• This paper focuses on developing wind energy prediction models.• Machine learning and Statistical techniques are applied in the study.• High accuracy of the proposed models is shown in terms of statistical measures.

Cited by 4 publications

References 27 publications

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Evaluation of TerraClimate gridded data in investigating the changes of reference evapotranspiration in different climates of Iran

Unlocking the potential: A review of artificial intelligence applications in wind energy

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