Revolutionizing solar-hydro-wind power forecasts in regional power systems with hybrid machine learning models
Journal
Solar Energy
Journal Volume
291
Start Page
113391
ISSN
0038-092X
Date Issued
2025-05-01
Author(s)
Abstract
As renewable energy's share in the global energy mix increases, accurate forecasting of power output is crucial for grid stability and effective energy management. In multi-step-ahead forecasts, non-stationary signals and noise often lead to error accumulation and propagation. To address this, we propose a hybrid model integrating Variational Mode Decomposition (VMD) with Long Short-Term Memory (LSTM) neural networks (VMD-LSTM). This model leverages VMD's signal decomposition strengths and LSTM's temporal forecasting capabilities, improving input stationarity and noise resistance. Focusing on a regional grid in Hunan province, China, we used hourly power output data from hydro, wind, and solar stations (2017–2022), divided into training and testing sets. The VMD-LSTM model, trained on decomposed power output data, generated forecasts up to 24 h ahead. Compared to the LSTM, VMD-LSTM demonstrated superior performance, with improvements in Nash-Sutcliffe efficiency coefficient (NSE) by 6.7%, 9.1%, and 8.2%, and reductions in root mean squared error (RMSE) by 9.2%, 4.7%, and 16.8% for hydropower, wind, and solar forecasts at the 24-hour horizon. This study provides a valuable tool for enhancing renewable energy integration, contributing to the stability and efficiency of power grid management systems in regions with diverse energy sources.
Subjects
Hydro-wind-solar system
Long short-term memory neural network
Machine learning
Power forecasts
Variational mode decomposition
SDGs
Publisher
Elsevier BV
Description
Article number: 113391
Type
journal article