Wavelet Transform Based Comparative Analysis of Wind Speed Forecasting Techniques
Authors :- Jay Chaudhari, Harsh S. Dhiman, Parth Suthar, K. Manjunath
Publication :- Renewable Energy Optimization, Planning and Control, 2021
The objective of this manuscript is to assess the performance of prominent wind forecasting methods by considering real-time data. In specific, forecasting techniques such as Simple Exponential Smoothing (SES), Autoregressive Integrated Moving Average (ARIMA), support vector regression (SVR), and Kalman Filtering-based approaches are implemented on three real-time data sets pertaining to different regions under different climatic conditions. The error between actual and forecasted wind speeds for the aforementioned methods is evaluated in terms of root mean square error (RMSE). Furthermore, the economic implication of each method is determined by calculating the penalty. A case study is carried out to compare the performance of each forecasting algorithm.