Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1160-1166· 0 citations· 10 references
Abstract
Proper prediction of photovoltaic (PV) power output is essential in ensuring the successful incorporation of solar energy in the smart grid systems and energy management systems. Current single-algorithm models are often ineffective to represent the compound non-linear interactions between meteorological variables, time variations and irradiance dynamics that cause solar generation variability. This paper presents the Solar-Adaptive Hybrid Ensemble (SAHE) which is a new two-stage stacking model that uses a new Random Forest (RF) base learner with an XG Boost residual-correction meta-learner, supplemented by solar-domain feature engineering such as clearness index, irradiance polynomial transforms, lagged target variables and cyclic temporal encodings. The SAHE framework has a Root Mean Squared Error (RMSE) equal to 23.8850 kWh, Mean Absolute Error (MAE) equal to 18.6632 kWh, coefficient of determination (R 2) equal to 0.8079, Mean Absolute Percentage Error (MAPE) equal to 8.7608% and Pearson Correlation Coefficient (PCC) equal to 0.9113 on the test set, compared to all comparison models, in all reported measures.
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
The increasing penetration of solar photovoltaic (PV) generation into modern power systems has created a growing need for accurate short-term PV power forecasting to support reliable grid operation, energy management, and renewable energy integration. This study presents a comparative analysis of three machine learning...
Subash Ranjan Kabat, Priyadarshi Das, Rashmita Lenka et al.· International Research Journ...· 0 citations
Variations in solar irradiance and module temperature significantly affect the performance and operational efficiency of large-scale photovoltaic (PV) power systems, especially in tropical regions. This study investigates the application of a Long Short-Term Memory (LSTM) network for accurate real-time power prediction...
A. Muhtar, S. Baqaruzi, P. Yunesti· Jurnal Elektronika dan Telek...· 0 citations
The proposed approach can be effectively utilised to optimise tilt angle selection, improve energy forecasting, and enhance the overall efficiency of solar photovoltaic systems.
N. Kumar, P. S. Paliyal, A. Yadav et al.· International Journal of Ene...· 0 citations
The transition toward renewable energy integration in power systems presents challenges in managing photovoltaic (PV) power output owing to variations in operating conditions. This study evaluated the performance of K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), with and wi...
Ilham Maridi, Fiky Anggara, Martati Martati· Jurnal Media Elektrik· 0 citations
The nature of solar radiation and the high penetration of photovoltaic (PV) systems in the smart electrical grid necessitate the development of models driven by historical operational data capable of precisely estimating the performance of PV systems. In this work, six proposed models are applied to predict the convers...
Bashar K. Hammad, S. Al-Dahidi, Mohammad Al-Abed· Solar· 0 citations
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