Solar Radiation Forecast Utilizing an Artificial Neural Network Model with Three Distinct Learning Algorithms

Solar energy, which has always been at the forefront, has discovered numerous uses in a variety of fields. One of the key targets of scientists and producers in the twenty-first decade is sustainable solar energy collecting. The maximization of solar energy is totally dependent on the radiation absorbed by the photovoltaic panels. Radiation is observed using numerous equipment and calculated using diverse methods. If the device is to be totally reliant on solar energy, it must be calculated far ahead. It is difficult to work because solar radiation is affected by various factors, including region as well as seasonality. In forecast scenarios, Artificial Neural Networks (ANN) is a popular approach among scientists. Therefore, this research provides a technique for estimating solar radiation that makes use of back-propagation algorithms. The data of 17 stations in Tamil Nadu, India, were acquired for analysis and split into three clusters: training, validation, and testing. This research is focused on nine input variables and one outcome variable. Solar radiation is estimated via feed-forward back-propagation in this case. The presented approach is ascertained for training techniques such as Levenberg Marquardt (LM), Bayesian-Regularization (BR), as well as Scaled Conjugate Gradient (SCG). In all three scenarios, the resulting statistical error values and regression values prove the adequacy of the presented approach. The Root Mean Square Error (RMSE) for the BR approach is found to be the lowest, with a value of 0.0013. Also, from all statistical error and regression values, the training approach BR produces the best value than LM and SCG training approaches.

• Record URL:
  • https://doi.org/10.4271/2023-01-5174
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Bhuvaneswari, M
  • Kumar, T J Prasanna
  • Gobikrishnan, U
  • Rajesh, S
  • Prabu, D Antony
  • Seenivasan, Madhankumar
• Conference:
  • International Conference on Trends in Automotive Parts Systems and Applications
  • Location: Kuniamuthur Coimbatore, India
  • Date: 2023-12-21 to 2023-12-22
• Publication Date: 2024-2-23

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Forecasting; Machine learning; Neural networks; Solar energy; Solar radiation
• Geographic Terms: Tamil Nadu (India)
• Subject Areas: Energy; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01914382
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2023-01-5174
• Files: TRIS, SAE
• Created Date: Apr 15 2024 8:37AM