Abstract
Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error).
Highlights
The current status of fossil fuel consumption and threatening impacts of conventional energy sources on the environment have motivated the researchers towards focusing on increased extraction of energy from rene– wable resources
We have focused on the statistical approach in our study to forecast the wind power density
A prediction model framework is proposed using five different neural network models to present and compare different forms of relationships in estimating wind power density based on 39 years of data from five different stations in Tamil Nadu, India
Summary
The current status of fossil fuel consumption and threatening impacts of conventional energy sources on the environment have motivated the researchers towards focusing on increased extraction of energy from rene– wable resources. Explo– ration of wind energy from offshore and onshore sites has attracted the attention of both resea–rchers and capitalists as a potential area of investment. This is fuelled by the ever-increasing demand for energy in variousproduction and service sectors [3]. As a rapidly growing source of energy, the current trend in wind energy extraction is expected to expand in the near future. This is one of the cheapest forms of energy, inappropriate placement of wind turbines may result in under-utilization of their capacity and can lead to huge loss of revenue. Some of the resear–chers have adopted novel techniques for determining the optimal location of the wind farms [6,7,8]
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