Affiliations 

  • 1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 5166616471, Iran
  • 2 GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI - Intelligent Systems Associate Laboratory, Polytechnic of Porto, P-4200-072, Porto, Portugal
  • 3 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 5166616471, Iran. mohammadi@ieee.org
  • 4 SYSTEC-ARISE Research Center for Systems and Technologies, Electrical and Computer Enginnering Department, Faculty of Engineering, University of Porto, P-4200 465, Porto, Portugal
  • 5 Department of Energy (AAU Energy), Aalborg University, 9220, Aalborg, Denmark
  • 6 Institute of High Voltage & High Current, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Environ Sci Pollut Res Int, 2024 Mar;31(12):18281-18295.
PMID: 37837598 DOI: 10.1007/s11356-023-30224-1

Abstract

Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.