Short term multi-steps wind speed forecasting for carbon neutral microgrid by decomposition based hybrid model

Abstract

Combining the benefits of an empirical mode decomposition (EMD), a convolutional neural network (CNN), and a long-short term memory (LSTM) neural network, this research proposes a hybrid model for forecasting short-term multistep ahead wind speed that can assist microgrids in achieving their low generation cost and carbon neutrality goals. Prior to EMD, historical wind speed time series were pre-processed, and then the resulting time series of wind speeds were decomposed into intrinsic mode functions (IMFs). Following that, a CNN-LSTM model that had been subjected to optimization was employed to carry out the training and subsequent evaluation (validation and implementation of the model). While CNN is able to directly extract the time series' intrinsic features, LSTM networks can fully leverage the data for improved prediction. In order to demonstrate the superiority of the proposed model, its results were compared to those of six other models (including hybrids and standalones), and the findings of the experiments provide empirical evidence that the hybrid EMD-CLSTM model proposed is superior. The strategy that has been proposed has also been utilized in industrial settings, where it has been found to be successful. The green energy management system (GEMS) of Leonics Co. Ltd. has incorporated the proposed model into their short-term decision-making and scheduling procedures in order to achieve their goals of a better energy mix, low-cost power generation, and carbon neutrality. Last but not least, the energy management system of the modified GEMS microgrid was upgraded to enable a greater penetration of wind energy sources without decreasing the microgrid's stability or reliability, making it possible for the system to eventually become carbon neutral.