Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particula...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2021, Vol.2021, p.1-10
Main Authors: Peiris, Amila T., Jayasinghe, Jeevani, Rathnayake, Upaka
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Ambient temperature
Artificial neural networks
Back propagation
Bias
Case studies
Climate change
Dependent variables
Electricity distribution
Energy resources
Energy sources
Forecasting
Fossil fuels
Greenhouse gases
Humidity
Hydroelectric power
Independent variables
Mathematical models
Neural networks
Regularization
Renewable resources
Variables
Wind direction
Wind farms
Wind power
Wind power generation
Wind speed
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Summary:Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE 
ISSN:2090-0147
2090-0155
DOI:10.1155/2021/5577547