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

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. Howe...

Full description

Saved in:
Bibliographic Details
Published inEnergies (Basel) Vol. 17; no. 16; p. 4174
Main 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
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
Subjects
Accuracy
Algorithms
Alternative energy sources
Artificial intelligence
clear sky irradiance
Deep learning
Electricity
Forecasting
forecasting PV power
Forecasting techniques
large-scale photovoltaic power plant
long short-term memory
Machine learning
Neural networks
Photovoltaic power generation
Power plants
PV power plant
Radiation
Regression analysis
Renewable resources
Solar energy
Solar radiation
Statistical methods
Support vector machines
Time series
Trends
Vietnam
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17164174