Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting
Copyright policy )Effect of a resampling method on the effectiveness of multi-layer neural network models in PV power forecasting
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.
- Ziane Achour University of Djelfa Algeria
- Ziane Achour University of Djelfa Algeria
MNN, Artificial neural network, Resampling, Artificial intelligence, Electricity Price and Load Forecasting Methods, Science, Organic chemistry, Quantum mechanics, Layer (electronics), Engineering, Artificial Intelligence, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning Methods for Solar Radiation Forecasting, Electrical and Electronic Engineering, Energy, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, Physics, Q, Load Forecasting, QA75.5-76.95, Photovoltaic Maximum Power Point Tracking Techniques, Power (physics), Computer science, Resampling Method, Chemistry, Electronic computers. Computer science, Computer Science, Physical Sciences, Short-Term Forecasting, Photovoltaic, Mathematics, Probabilistic Forecasting, Forecasting
MNN, Artificial neural network, Resampling, Artificial intelligence, Electricity Price and Load Forecasting Methods, Science, Organic chemistry, Quantum mechanics, Layer (electronics), Engineering, Artificial Intelligence, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning Methods for Solar Radiation Forecasting, Electrical and Electronic Engineering, Energy, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, Physics, Q, Load Forecasting, QA75.5-76.95, Photovoltaic Maximum Power Point Tracking Techniques, Power (physics), Computer science, Resampling Method, Chemistry, Electronic computers. Computer science, Computer Science, Physical Sciences, Short-Term Forecasting, Photovoltaic, Mathematics, Probabilistic Forecasting, Forecasting
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