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- Title
Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting.
- Authors
Bui Duy, Linh; Nguyen Quang, Ninh; Doan Van, Binh; Riva Sanseverino, Eleonora; Tran Thi Tu, Quynh; Le Thi Thuy, Hang; Le Quang, Sang; Le Cong, Thinh; Cu Thi Thanh, Huyen
- Abstract
This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables.
- Subjects
PHOTOVOLTAIC power systems; STANDARD deviations; RECURRENT neural networks; SOLAR radiation; SOLAR energy
- Publication
Energies (19961073), 2024, Vol 17, Issue 16, p4174
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en17164174