Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations

Abstract

Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%–31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design.

Studying the gender effect on apparent mass during whole-body vibration using ANN

“…The human age, weight, body mass index (BMI), height, and the frequency level, all beside the gender are used to study the BioR of the subjects while exited by a vibration platform. Beside the many usages [44,[50][51][52][53][54][55][56][57][58][59], previously, the transmissibility in all three directions were modeled using Artificial Neural Network (ANN). However, up to the knowledge of the authors, the effect on the apparent mass was not studied so far.…”

Design a speed reducer gearbox using chameleon swarm algorithm

“…In recent years, metaheuristic algorithms have gained significant attention for solving complex optimization problems in various fields [1][2][3][4][5][6], including engineering [7], business [8], finance [9], the Internet of Things [10], and design. Additionally, machine learning techniques have been widely incorporated in conjunction with metaheuristic algorithms to further enhance their performance and applicability [11][12][13][14][15][16][17][18][19][20][21][22][23]. These algorithms are inspired by natural phenomena and exhibit robustness and versatility in finding optimal solutions in highly complex and dynamic environments [11,24,25].…”

“…Additionally, machine learning techniques have been widely incorporated in conjunction with metaheuristic algorithms to further enhance their performance and applicability [11][12][13][14][15][16][17][18][19][20][21][22][23]. These algorithms are inspired by natural phenomena and exhibit robustness and versatility in finding optimal solutions in highly complex and dynamic environments [11,24,25]. The Chameleon Swarm Algorithm (CSA) is a novel metaheuristic algorithm that has been designed specifically for optimizing complex problems, such as the case of this paper which is speed reducer gearbox design in autonomous vehicles.…”

Design a wing flange lever mechanism using Giza pyramid algorithm

“…Evolutionary memetic algorithms were effectively applied to optimize discrete routing problems, such as the traveling salesman problem, and involved the use of memetic algorithm, metaheuristic profile, and trajectory-based optimization methods [11][12][13][14][15][16][17][18]. With the integration of machine learning [19][20][21][22][23][24][25][26], evolutionary models can leverage advanced machine learning techniques to enhance their capabilities. Machine learning can be employed to improve the efficiency and effectiveness of genetic algorithms, allowing for faster convergence to optimal solutions and better exploration of the solution space.…”