A multimodal approach to chaotic renewable energy prediction using meteorological and historical information
Description
Wind energy, which exhibits non-stationarity, randomness, and intermittency, is inextricably linked to meteorological data. The wind power series can be broken down into several subsequences using data decomposition techniques to make forecasting simpler and more accurate. Because of this, a single prediction model does not perform well in extracting hidden information from each subsequence. To predict different frequency series, this research employed shallow and deep learning models and proposed an improved hybrid wind power prediction model based on secondary decomposition, extreme learning machines (ELM), convolutional neural networks (CNN), and bidirectional long short-term memory (BiLSTM). To begin, secondary decomposition was employed to break down the wind power series into several components. The ELM was used to forecast the low-frequency components. Following that, CNN was utilized to reintegrate the input characteristics of the high-frequency components, followed by BiLSTM prediction. Finally, the forecasting values for each component were added to generate the final prediction results. Requirements: Python 3.6.x TensorFlow 1.19.0 Numpy 1.19.5 Pandas 1.0.5 Keras 2.2.5 scikit-learn 0.23.1 Matplotlib 3.2.2 Reference: The detailed information of CEEMDAN and VMD are shown in: https://github.com/vrcarva/vmdpy https://github.com/laszukdawid/PyEMD Data availability: Supplementary data to this research can be found online at https://opendata-renewables.engie.com