Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

Linh Bui Duy, Ninh Nguyen Quang (), Binh Doan Van, Eleonora Riva Sanseverino, Quynh Tran Thi Tu, Hang Le Thi Thuy, Sang Le Quang, Thinh Le Cong and Huyen Cu Thi Thanh
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Linh Bui Duy: Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam
Ninh Nguyen Quang: Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam
Binh Doan Van: Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam
Eleonora Riva Sanseverino: Engineering Department, University of Palermo, 90128 Palermo, Italy
Quynh Tran Thi Tu: Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
Hang Le Thi Thuy: Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
Sang Le Quang: Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
Thinh Le Cong: Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
Huyen Cu Thi Thanh: Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam

Energies, 2024, vol. 17, issue 16, 1-22

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.

Keywords: long short-term memory; clear sky irradiance; large-scale photovoltaic power plant; forecasting PV power; PV power plant; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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