Abstract
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 < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.
Highlights
Introduction26.2% of the 2018 world’s energy demand was supplied by renewable energies, and it is forecasted to increase the percentage up to 45% by the year 2040 [5]
If the output of wind power generation can be accurately forecasted, the negative influences that wind power brings to the grid can be reduced by a large extent [14]. e accuracy of wind power prediction is important for balancing the power generation as well [15]
Calibration of Scaled Conjugate Gradient (SCG) training algorithm shows a better performance at validation percentage of 10% with the minimum mean squared error (MSE) of 0.017 at epoch 21
Summary
26.2% of the 2018 world’s energy demand was supplied by renewable energies, and it is forecasted to increase the percentage up to 45% by the year 2040 [5]. Wind energy is one of the best solutions for global warming because it is completely pollution-free and causes no greenhouse effects [6]. It has become a cost-effective approach due to the rise in fossil fuel prices [7]. Sri Lanka, as a country, is in discussions for the generation of renewable energies, and it is proposed to generate renewable energy to meet 100% of the country’s demand by 2050 [9]. Accurate predictions are helpful to the government, policymakers, and other responsible authorities for taking necessary actions
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