Short Term Wind Forecasting Using Machine Learning Models with Noise Assisted Data Processing Method

In order to address the complex and intermittent nature of wind, this thesis proposes an innovative approach to enhance the accuracy of short-term wind forecasting. By leveraging the strengths of different methods while mitigating their weaknesses, a robust hybrid model is developed. The methodology incorporates Empirical Mode Decomposition (EMD), a data-adaptive denoising technique, to break down the signals into meaningful components called Intrinsic Mode Functions (IMFs), along with a residue. However, EMD is known to suffer from the mode mixing problem, where different scales of signals are erroneously mixed within IMFs, causing signal intermittency. To overcome this challenge, Ensemble Empirical Mode Decomposition is introduced, utilizing an ensemble of white noise to establish a uniform reference frame in the time-frequency space. By doing so, the added noise effectively collates signal portions with similar scales into a single IMF. Subsequently, the IMFs and residue obtained from both EMD and Ensemble Empirical Mode Decomposition are fed into a Convolutional Neural Network (CNN). The performance of this hybrid model is then compared against benchmark models such as Bi LSTM and LSTM. Evaluation is conducted based on two critical factors: performance metrics including MSE, MAE, RMSE, MAPE, and loss, as well as the time required for testing. Through a comprehensive analysis of these factors, the superior performance of the hybrid model is determined, thereby enhancing the prospects of reliable wind forecasting.