Open AccessDetermination of a Renewable Energy System Capacity Factor Using Machining Learning Algorithms
Author(s) -
Desmond Eseoghene Ighravwe,
Daniel Mashao
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1107/1/012005
Subject(s) - artificial neural network , backpropagation , algorithm , support vector machine , radial basis function , machine learning , computer science , kernel (algebra) , artificial intelligence , correlation coefficient , wind power , engineering , mathematics , combinatorics , electrical engineering
Across the world, machining learning (ML) algorithms, such as support vector machines (SVR) and artificial neural networks (ANN), are among scientific tools for the fourth industrial revolution, or Industry 4.0, campaign. These algorithms have wide engineering applications, but their potentials in energy management are still evolving. Hence, this study investigates the performance of SVR and ANN algorithms as predictive models for wind turbines capacity factor (CF) estimation. Five independent parameters-wind speed, power density, turbulence intensity, installed capacity, and wind shear - were used as input parameters for this estimation problem. Polynomial, radial basis function (RBF), and linear kernels were used to train an SVR model that estimates CF, while Adams was used to optimize the performance of a backpropagation ANN model. These models’ applicability was evaluated using data sets from eight locations. This study used correlation coefficient used to compared the model performance. The RBF trained SVR model performed better than the other kernels during model training, but the linear kernel trained SVR model performed better than the other kernels during testing model. When these kernels performances were compared with a single hidden layer ANN model, the ANN model results were better than these kernels results.