@article{Tuyen_Vu Xuan Son_Le Viet_2021, title={Photovoltaic Power Generation Forecasting Utilizing Long Short Term Memory}, volume={1}, url={https://mca-journal.org/index.php/mca/article/view/21}, abstractNote={<p>The modernization of the world has considerably reduced the prime sources of energy, such as coal, diesel and gas. Thus, alternative energy<br>sources based on renewable energy have been the main concentration nowadays to address the world’s energy demand and at the same time to<br>restrict global warming. Among these renewable energies, solar energy is the main source used to generate electricity through photovoltaic<br>(PV) systems. However, the output of PV power is highly intermittent. Thus accurately forecasting the output of PV systems is an important<br>requirement to ensure the stability and reliability of the grid. This study develops and validates a short-term PV power forecasting model<br>by using the combination of a genetic algorithm (GA) and Long Short Term Memory (LSTM). The performance of the proposed model is<br>compared with LSTM baseline model by three errors (Root mean square error(RMSE), Mean absolute error (MAE), and Mean absolute<br>percentage error (MAPE)) in two case studies.</p>}, number={2}, journal={Measurement, Control, and Automation}, author={Tuyen, Nguyen-Duc and Vu Xuan Son, Huu and Le Viet, Thinh}, year={2021}, month={May} }