Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

Domaine:

environment and energy

Type de record:

paper
Créateur:
KhaAmrAhm
Éditeur:
MDP
Hôte:
The world’s technological and economic advancements have led to a sharp increase in the demand for electrical energy. Saudi Arabia is experiencing rapid economic and demographic growth, which is resulting in higher energy needs. The limits of fossil fuel reserves and their disruption to the environment have motivated the pursuit of alternative energy options such as wind energy. In order to regulate the power system to maintain safe and dependable operation, projections of current and daily power generation are crucial. Thus, this work focuses on wind power prediction and the statistical analysis of wind characteristics using wind data from a meteorological station in Makkah, Saudi Arabia. The data were collected over four years from January 2015 to July 2018. More than twelve thousand data points were collected and analyzed. Layout and wake effect studies were carried out. Furthermore, the near wake length downstream from the rotor disc between 1 and 5 rotor diameters (1D to 5D) was taken into account. Five robust machine learning algorithms were implemented to estimate the potential wind power production from a wind farm in Makkah, Saudi Arabia. The relationship between the wind speed and power produced for each season was carefully studied. Due to the variability in the wind speeds, the power production fluctuated much more in the winter. The higher the wind speed, the more significant the difference in energy production between the five farm layouts, and vice versa, whereas at a low wind speed, there was no significant difference in the power production in all of the near wake lengths of the 1D to 5D rotor diameters downstream from the rotor disc. Among the utilized prediction models, the decision tree regression was found to have the best accuracy values in all four utilized evaluation metrics, with 0.994 in R-squared, 0.025 in MAE, 0.273 in MSE, and 0.522 in RMSE. The obtained results were satisfactory and provide support for the construction of several wind farms, producing hundreds of megawatts, in Saudi Arabia, particularly in the Makkah Region.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similaires

Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case StudyA comparative study of social inequalities in education as an effect of Covid-19 pandemic: A case of schools in Saudi Arabia and KenyaA hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabiasikirusiyanbola/Wind-Turbine-Power-PredictionSTATISTICAL ANALYSIS AND ANN-BASED PREDICTION OF WIND POWER GENERATION: A CASE STUDY OF PEMBA ISLAND, ZANZIBAR, TANZANIA.Small 500 kW onshore wind farm project in Kribi, Cameroon: Sizing and checkers layout optimization model

Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an averag

A comparative study of social inequalities in education as an effect of Covid-19 pandemic: A case of schools in Saudi Arabia and Kenya

The Coronavirus epidemic is a major source of concern for global education systems and their ability

A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia

Type 2 diabetes (T2D) is considered a significant global health concern. Hemoglobin A1c level (HbA1c

sikirusiyanbola/Wind-Turbine-Power-Prediction

An XGBoost model for predicting wind turbine power output in Sub-Saharan Africa #Wind-Turbine_Powe

STATISTICAL ANALYSIS AND ANN-BASED PREDICTION OF WIND POWER GENERATION: A CASE STUDY OF PEMBA ISLAND, ZANZIBAR, TANZANIA.

This research work proposes the integrated statistical and ANN approach for the evaluation

Small 500 kW onshore wind farm project in Kribi, Cameroon: Sizing and checkers layout optimization model