Research Article
Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam
@ARTICLE{10.4108/eai.29-3-2021.169166, author={Ninh Nguyen Quang and Linh Bui Duy and Binh Doan Van and Quang Nguyen Dinh}, title={Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={36}, publisher={EAI}, journal_a={EW}, year={2021}, month={3}, keywords={Long Short -- Term Memory, Industrial PV power plant, Forecasting PV power, Artificial Intelligence}, doi={10.4108/eai.29-3-2021.169166} }- Ninh Nguyen Quang
Linh Bui Duy
Binh Doan Van
Quang Nguyen Dinh
Year: 2021
Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam
EW
EAI
DOI: 10.4108/eai.29-3-2021.169166
Abstract
This paper uses recurrent neural network (Long Short – Term Memory - LSTM network) to build a model to forecast short-term generation capacity of Phong Dien solar power plant, (48 MWp – 35 MWAC) located in Thua Thien Hue Province, Viet Nam, with input factors including meteorological parameters. The authors conducted experiments to find the optimal structure of the model corresponding to the conditions of the plant and the data collection. Through this model, meteorological forecast data sets from commercial suppliers were used to forecast the plant's output power. The comments about the result as well as the further study direction are analysed and suggested.
Copyright © 2021 Ninh Nguyen Quang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
