Jul 2026· 2026 11th International Conference on Applying New Technology in Green Buildings (ATiGB)· pp. 1112-1116· 0 citations· 16 references
Abstract
The successful forecast of the solar photovoltaic (PV) power processing is essential in increasing grid stability, and in the ultimate inclusion of renewable energy. This paper is a research project aiming to introduce a hybrid classifier of Random Forest (RF), K-Nearest Neighbors (KNN), and Elastic Net (EN) to enhance multi-regional solar PV power forecasting using time-series. Preprocessing of Hourly PV generation data was done by taking out the temporal feature like hour, day and month to improve forecasting. To compare the proposed hybrid model with individual algorithms, the coefficient of determination (R2), mean absolute error (MAE) were used to evaluate the model. The results of the experiment indicate that the hybrid model had an R 2 of 0.91, which was higher than RF (0.88), KNN (0.82), and EN (0.79), and nearly halved the MAE, relative to single models. The results prove that the combination of multiple regression methods would lead to better prediction robustness, as well as lower variance and increase forecasting accuracy, which makes the suggested method an appropriate tool to study smart grids and sustainable energy planning.
A comparative analysis of three machine learning models, namely Artificial Neural Network, Random Forest, and Support Vector Regression, for short-term solar PV power forecasting shows that Random Forest provides superior forecasting performance compared with ANN and SVR, achieving lower prediction errors and a higher...
Subash Ranjan Kabat, Priyadarshi Das, Rashmita Lenka et al.· International Research Journ...· 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
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
This study demonstrates the effectiveness of gradient boosting models and neural networks for accurately forecasting solar production in Meknes, thus offering promising prospects for the deployment of solar solutions in Morocco.
Nasyra Elouastani, M. Moussaoui, S. Amraqui· EPJ Web of Conferences· 0 citations
This research provides a direct comparison of five machine learning algorithms, that is, Decision Tree Regression (DTR), Multiple Linear Regression (MLR), Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) for forecasting PV power output in Jazan, Saudi Arabia.
Abdoalateef Alzhrani, A. Mas’ud, M. K. A. Kamarudin et al.· Energy Science & Enginee...· 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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