Abstract
As the solar photovoltaic (PV) penetration level increases in smart grids, precise and computationally efficient short-term forecasting becomes essential to aid operational planning and real-time energy management. However, the power produced by PV is highly nonlinear and stochastic due to variations in weather factors, which weakens the performance of single forecasting models. The aim of this work is to propose a stacked ensemble regression model that combines Gradient Boosting and XGBoost (Extreme Gradient Boosting) as base learners, with Ridge Regression as the meta-learner, for very short-term PV power prediction. The model operates using meteorological and operational parameters, such as temperature, humidity, wind profile, cloudiness distribution, and solar situation. Standard preprocessing steps (missing value imputation, feature selection, and normalization) are adopted to facilitate stable model training. An empirical study is carried out using real-world PV generation data, and the results are compared with popular gradient boosting algorithms such as Gradient Boosting, XGBoost, LightGBM (Light Gradient Boosting Machine), and CatBoost (Categorical Boosting), and machine learning models such as multilayer perceptron (MLP) and LSTM, using k-fold cross-validation. The boosted ensemble improves predictive accuracy, achieving MAE = 0.042 ± 0.002, MSE = 0.0031 ± 0.0002, and R² = 94% ± 1% under the experimental conditions used in this work. Nonparametric tests (i.e., the Wilcoxon signed-rank test and the Friedman test) show that such improvements are statistically significant (p < 0.05). Moreover, the inference latency of the proposed model is quite low, which demonstrates its suitability for near-real-time deployment in real-world smart grid scenarios. According to experimental results, lightweight ensemble learning can stand as a competitive and practical alternative to complicated deep learning methods for short-term PV power forecasting when data availability and computational budget are taken into account.
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Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
Abbreviations
- PV:
-
Photovoltaic
- ML:
-
Machine learning
- GB:
-
Gradient boosting
- XGBoost:
-
Extreme gradient boosting
- LightGBM:
-
Light gradient boosting machine
- CatBoost:
-
Categorical boosting (developed by Yandex)
- LSTM:
-
Long short-term memory
- MAE:
-
Mean absolute error
- MSE:
-
Mean squared error
- R2 or R2 :
-
Coefficient of determination
- RNN:
-
Recurrent neural network
- AI:
-
Artificial intelligence
- ARIMA:
-
AutoRegressive Integrated Moving Average
- SARIMA:
-
Seasonal AutoRegressive Integrated Moving Average
- ANN:
-
Artificial neural network
- SVM:
-
Support vector machine
- GBM:
-
Gradient boosting machine
- ReLU:
-
Rectified linear unit
- CNN:
-
Convolutional neural network
- GAN:
-
Generative Adversarial Network
- BERT:
-
Bidirectional encoder representations from transformers
- SR:
-
Stacked regressor
- IQR:
-
Interquartile range
- Q1:
-
First Quartile (25th Percentile)
- Q3:
-
Third Quartile (75th Percentile)
- ε:
-
Residual
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T. Mariprasath, Rohini G: Conceptualization, Methodology, Software, Visualization, Investigation, Writing- Original draft preparation. Seif Al Bustanji: Data curation, Validation, Supervision, Resources, Writing - Review & Editing. Ievgen Zaitsev: Project administration, Supervision, Resources, Writing - Review & Editing.
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Rohini, G., Mariprasth, T., Bustanji, S.A. et al. A stacked Gradient Boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47042-3
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DOI: https://doi.org/10.1038/s41598-026-47042-3
