Relative Humidity Effect on the Extracted Wind Power for Electricity Production in Nassiriyah City

Salih, Abdul Kareem Mahdi; Majli, Abdullh Saiwan

doi:10.4172/2090-4541.1000199

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Still, Abdul-Kareem et.al. [28] found that inadequate the amount of moisture composition towards wind energy sources at average annual established the minimum altitude requirement that was consumed in the power plant of Nasiriyah (44 m for α = 0.3; 32 m for α = 0.4). Meanwhile, as the altitude is visible at a higher position, it has been growing.…”

Section: Humiditymentioning

confidence: 99%

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

2022

Inf. Sci. Lett.

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Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R²) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection.

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Section: Humiditymentioning

confidence: 99%

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

2022

Inf. Sci. Lett.

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A deep learning-enhanced framework for sustainable hydrogen production from solar and wind energy in the Moroccan Sahara: Coastal regions focus

Adeli,

Nachtane,

Faik

et al. 2024

Energy Conversion and Management

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Analytical Simulation Approach to Evaluate the Ambient Humidity Effects on the Performance of a Wind Accelerator

Al-Hinai,

Clement Varaprasad,

Vinod Kumar

2024

IOP Conf. Ser.: Earth Environ. Sci.

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The power production of wind turbines is experiencing an upward trend. However, on the other hand, the wind turbine’s performance slows down due to unseen changes in the environment. Humidity is one of the environmental factors affecting the performance of wind turbines. When humidity levels are high in the atmosphere, air is less dense. This air is lighter, and it results in reduced air pressure. A remedy to increase the air pressure in the direction of wind flow is to locate a wind accelerator, an aerodynamic device before the wind turbine to amplify the incoming wind speed and reduce the impact of humidity on the wind energy. This study intends to investigate the impact of environmental humidity on the performance of the wind accelerator. The study includes modelling the wind accelerator using Ansys 2023R1, to simulate different humidity levels. An analytical simulation is conducted to describe the aerodynamic behaviour of the wind accelerator in response to the changes in ambient humidity. The air at different percentage levels of humidity is passed through the proposed wind accelerator geometry to observe the wind speed variation and kinetic energy of the wind. The simulation study reveals that the wind speed and kinetic energy show higher values when the humidity is at a lower level, while the opposite is true when the humidity is higher. This emphasizes how important it is to consider all environmental elements while designing and developing wind turbines and wind accelerators for optimal performance subjected to various climatic conditions.

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Relative Humidity Effect on the Extracted Wind Power for Electricity Production in Nassiriyah City

Cited by 4 publications

References 6 publications

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

A deep learning-enhanced framework for sustainable hydrogen production from solar and wind energy in the Moroccan Sahara: Coastal regions focus

Analytical Simulation Approach to Evaluate the Ambient Humidity Effects on the Performance of a Wind Accelerator

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