Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 796-801· 0 citations· 28 references
Abstract
Accurate renewable power forecasting is essential for grid operation, reserve scheduling, and renewable integration. This paper proposes a constraint-guided residual forecasting framework that combines a domain-informed baseline with data-driven residual correction for multi-horizon probabilistic forecasting. A Gradient Boosting Regression Tree (GBRT) model is used as a classical residual benchmark, while a Residual PatchTST model captures temporal dependencies from numerical weather prediction features, engineered time variables, and site information. Final forecasts are reconstructed by adding the predicted residual to the baseline and enforcing nonnegative outputs within site-level capacity limits. Across PV sites, GBRT reduces mean RMSE from 0.0909 to 0.0798 and mean MAE from 0.0400 to 0.0358, while wind RMSE decreases from 0.2574 to 0.1479. The deep probabilistic model also delivers stable multi-horizon performance and useful uncertainty intervals. These results show that residual learning with constraint-guided reconstruction provides accurate and operationally meaningful renewable power forecasts.
Accurate short-term solar irradiance forecasting is essential for reliable operation of renewable-dominated energy systems, where scheduling, flexibility management, and grid operation depend on prediction stability. Solar irradiance prediction remains challenging because it is influenced by both deterministic solar ra...
Tareq Anwar Shikdar, Hannu Laaksonen· IEEE Access· 0 citations
: Accurate probabilistic wind farm power forecasting is essential for reserve scheduling, dispatch decision-making, and risk-aware operation under high levels of wind power penetration. However, short-term wind power sequences exhibit strong nonstationarity, heterogeneous environmental variables exhibit time-varying pr...
Zhong Chen, Zenan Wang, Siyu Chen et al.· Energy Engineering· 0 citations
This study presents a physically constrained robustness evaluation framework, using virtual PV power as a controlled response variable to isolate the propagation of input uncertainty from confounders at the plant level, and shows that sequence models provide stronger noise filtering and temporal resilience than a stron...
Dandan Chen, Yan Zhao, Xuepeng Chen· arXiv.org· 0 citations
Given wind energy’s growing significance in the world’s energy structure, the demand for high-precision forecasting is more urgent than ever. However, wind power’s inherent non-stationarity is linked to complex and variable meteorological conditions, which pose significant challenges for accurate forecasting. The accur...
Boni Wang, Ming-Jian Zeng, H. Shan et al.· Journal of Marine Science an...· 0 citations
A hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy, and demonstrates that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photov...
Khawla Oufrit, A. Mouadili, M. Zazoui· EPJ Web of Conferences· 0 citations
A lightweight hybrid deep learning framework that combines a transformer encoder and Gated Rrecurrent Uunit (GRU) network for short-term solar radiation forecasting in Makkah and Madinah, Saudi Arabia outperformed ARIMA, LSTM, GRU, and XGBoost models while maintaining stable performance across varying weather condition...
Nasser S. Alkhaldi· Processes· 0 citations
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