Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting


  • Abderrahman Bensalem Applied Automation and Industrial Diagnostics Laboratory LAADI Faculty of Science and Technology, University of Djelfa, Djelfa, Algeria
  • Toual Belgacem
  • Abdellah Kouzou
  • Zakaria Belboul




Photovoltaic, MNN, Forecasting, Resampling Method


The primary aim of this study was to explore the impact of employing the K-fold Cross Validation resampling method in contrast to the hold-out set validation approach on the efficacy of forecasting models utilizing Multi-layer Neural Networks (MNN) for predicting photovoltaic (PV) output power. Real data sourced from southern Algeria was utilized for this purpose. The performance of various configurations of MNN models, with differing learning rate values, was evaluated using the coefficient of variation of Root Mean Square Error (CV(RMSE)). The findings consistently demonstrate that models developed using K-fold Cross Validation exhibited superior performance across most scenarios. These results underscore the potential advantages of leveraging such resampling techniques in terms of both generalization and robustness of forecasting models.


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How to Cite

Bensalem A, Belgacem T, Kouzou A, Belboul Z. Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting . EAI Endorsed Trans Energy Web [Internet]. 2024 Jun. 18 [cited 2024 Jul. 13];11. Available from: https://publications.eai.eu/index.php/ew/article/view/3705