Jul 2026· IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies· pp. 221-225· 0 citations· 29 references
Abstract
Various elements, such as solar irradiation, angle of incidence, ambient temperature, wind speed and direction, shading effects, panel degradation, and other environmental and operational parameters, may substantially affect the efficiency of photovoltaic (PV) systems. This paper examines critical factors influencing PV performance, including cooling solutions, temperature coefficient evaluation, geographical effects, and sophisticated materials, emphasizing the effects of solar irradiation and ambient temperature. The potential of artificial intelligence (AI) in forecasting PV system efficiency is examined. Four Machine Learning (ML) models are used; the Neural Network outperforms others in performance, while the Fine Tree model offers superior training time. Feature importance analysis indicates that prioritizing solar irradiance enhances PV prediction accuracy, while excluding low-impact variables, such as wind direction, enables simpler, cost-effective, and reliable real-world PV prediction systems.
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
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
Reliable photovoltaic power prediction is increasingly important for integrating variable solar generation into modern electricity systems, yet many operational installations have only limited historical data. This study developed a data-efficient, explainable, and uncertainty-aware machine-learning framework using 412...
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 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
The results demonstrate that incorporating physically meaningful orientation-aware features substantially improves forecasting accuracy for heterogeneous rooftop PV systems and can improve the accuracy of distributed PV generation forecasts and net demand forecasts at the distribution level compared to traditional aggr...
H. Çevik, Mustafa Arslan, Mehmet Çunkaş· PeerJ Computer Science· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
