Short-Medium-Term Solar Irradiance Forecasting with a CEEMDAN-CNN-ATT-LSTM Hybrid Model Using Meteorological Data DOI Creative Commons

M Mora Camacho,

Jorge Maldonado-Correa, Joel Torres-Cabrera

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1275 - 1275

Published: Jan. 26, 2025

In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition clean energy sources such as solar and wind. However, these energies face challenges cloud cover, precipitation, wind speed, temperature, which introduce variability intermittency in power generation, making integration into interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- medium-term irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) extract intrinsic seasonal patterns irradiance. addition, it employs encoder-decoder framework that combines convolutional neural networks (CNN) capture spatial relationships between variables, an attention mechanism (ATT) identify long-term patterns, long short-term memory (LSTM) network dependencies time series data. This has been validated using meteorological data more than 2400 masl region characterized by complex climatic conditions south Ecuador. It was able predict at 1, 6, 12 h horizons, mean absolute error (MAE) 99.89 W/m2 winter 110.13 summer, outperforming reference methods this study. These results demonstrate our represents progress contributing scientific community field environments high its applicability real scenarios.

Language: Английский

Wind and Solar Power Generation Forecasting Based on Hybrid CNN-ABiLSTM, CNN-Transformer-MLP Models DOI
Tasarruf Bashir,

Huifang Wang,

Mustafa Tahir

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 122055 - 122055

Published: Nov. 1, 2024

Language: Английский

Citations

13
Short-Medium-Term Solar Irradiance Forecasting with a CEEMDAN-CNN-ATT-LSTM Hybrid Model Using Meteorological Data DOI Creative Commons

M Mora Camacho,

Jorge Maldonado-Correa, Joel Torres-Cabrera

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1275 - 1275

Published: Jan. 26, 2025

In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition clean energy sources such as solar and wind. However, these energies face challenges cloud cover, precipitation, wind speed, temperature, which introduce variability intermittency in power generation, making integration into interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- medium-term irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) extract intrinsic seasonal patterns irradiance. addition, it employs encoder-decoder framework that combines convolutional neural networks (CNN) capture spatial relationships between variables, an attention mechanism (ATT) identify long-term patterns, long short-term memory (LSTM) network dependencies time series data. This has been validated using meteorological data more than 2400 masl region characterized by complex climatic conditions south Ecuador. It was able predict at 1, 6, 12 h horizons, mean absolute error (MAE) 99.89 W/m2 winter 110.13 summer, outperforming reference methods this study. These results demonstrate our represents progress contributing scientific community field environments high its applicability real scenarios.

Language: Английский

Citations

1