Vietnam Academy of Science and Technology

Project's information

Project's title	Research on methodology and building software to forecast the short-term output power of solar power plants using artificial intelligence
Project’s code	VAST07.01/21-22
Research hosting institution	Institute of Energy Science
Project leader’s name	Dr. Nguyen Quang Ninh
Project duration	01/01/2021 - 31/12/2022
Project’s budget	600 million VND
Classify	Grade A
Goal and objectives of the project	Research on methodology and building software to forecast the short-term output power of solar power plants using artificial intelligence
Main results	- Theoretical results: In this study, the short-term forecasting methods of the solar power plant's output power using artificial intelligence have been reviewed, presented details, and selected the suitable methodology to apply for an industrial-scale solar power plant in Vietnam. Based on the Long Short-Term Memory method, LSTM, the authors have proposed a solution to process input data in cases the solar power plant operates in curtailment conditions, and proposed a new training algorithm to archive better prediction results. Then a forecasting software based on the proposed method has been built, and some calculating experiments according to the proposed scenarios: one day in the rainy season, one day in the dry season, one month in the rainy season, and one month in the dry season have been carried out for a solar power plant in Vietnam. The results show that the predicted power is quite close to the actual generated power with the Mean Absolute Percentage Error, MAPE, below 10%, meeting the technical requirements, and the software works stably under experimental conditions.
Novelty and actuality and scientific meaningfulness of the results	The main purpose of this study is not only to suggest the direction of selecting suitable features and structure of the LSTM model but also to create a feasible procedure to forecast the short-term output power of a solar power plant in Vietnam. In this study, the authors focus on advanced training techniques for LSTM neural networks by proposing the use of validation sets and the application of GHI interval division techniques, adding the P/GHI factor in the process of processing data collected initially under the curtailment conditions. The results show that the proposed new method is more efficient than the old method with both MAPE (Mean Absolute Mean Percentage Error) and RMSE (Original Mean Squared Error) forecasting errors.
Products of the project	- Scientific papers in referred journals (list): + Linh Bui Duy, Ninh Nguyen Quang, Binh Van Doan, Eleonora Riva Sanseverino, Forecasting energy output of a solar power plant in curtailment condition based on LSTM using P/GHI coefficient and validation in training process, a case study in Vietnam, Electric Power Systems Research Volume 213, 2022, 108706 + Ninh Quang Nguyen, Linh Duy Bui, Doan Van Binh, Eleonora Riva Sanseverino, Dario Di Cara and Quang Dinh Nguyen, A new method for forecasting energy output of a large-scale solar power plant based on Long Short-Term Memory networks – a case study in Vietnam, Electric Power Systems Research, Volume 199, October 2021, 107427 + Ninh Nguyen Quang, Linh Bui Duy, Binh Van Doan, Quang Dinh Nguyen, Applying Artificial Intelligence in Forecasting the Output of Industrial Solar Power Plant in Vietnam, EAI Endorsed Transactions on Energy Web, http://dx.doi.org/10.4108/eai.29-3-2021.169166, 2021 + Linh Bui Duy, Ninh Nguyen Quang, Binh Doan Van, Khanh Pham Tuan, Duong Dinh Le, Evaluating the Effectiveness of the Solar Power Plant Output Forecasting Model Based on LSTM Method Using Validation in Different Seasons of the Year in Vietnam, GMSARN International Journal, 2022 (accepted) - Technological products (describe in details: technical characteristics, place): Software to forecast the short-term output power of solar power plants in Vietnam. The software is stored in the USB.
Research area	The results of the project can be applied to solar power plants in Vietnam. Forecasting software will help solar power plant operators predict the value of output power reduction during the plant's operation. Based on that, solar power plant owners can make a reasonable operation plan and get maximum benefits in curtailment conditions.
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