https://doi.org/10.1140/epjp/s13360-024-05730-x
Regular Article
Enhancing the accuracy of global horizontal irradiance estimation model using convolutional neural network coupled with wavelet transform
1
Department of Electrical and Electronics Engineering, G. L. Bajaj Institute of Technology and Management, 121310, Greater Noida, India
2
Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, 110078, Dwarka, New Delhi, India
Received:
9
September
2024
Accepted:
8
October
2024
Published online:
24
October
2024
One of the most important tasks for managing and producing solar energy as a fossil fuel substitute is the accurate estimation of global horizontal irradiance (GHI). To improve the accuracy of GHI estimation, this work highlights the significance of investigating cutting-edge deep learning techniques and their combinations such as wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron (WMLP), wavelet gated recurrent unit (WGRU), wavelet convolutional neural network is assessed for modeling global horizontal irradiance using various input combinations of climatic parameters for Rajasthan, a city situated in India. For an accurate assessment of the prediction accuracy of the proposed models, two robust statistical metrics such as mean absolute error (MAE) and root mean squared error (RMSE) are employed. With significant percentage decreases in MAE and RMSE, the results demonstrated that the WCNN technique outperformed the other models in estimating GHI. It is observed that the percentage reduction in MAE and RMSE for the standalone and wavelet-based models are (48.58, 15.67) for CNN, (63.86, 42.75) for GRU, (91.12, 22.09) for LSTM, and (33.40, 50.26) for MLP (52.80,76.34) for WLSTM, (50.14,13.63) for WMLP and (24.32,22.49) for WGRU, respectively. This study demonstrated significant improvements in prediction accuracy attained by hybridizing wavelet transform techniques with the CNN model. Based on climatic characteristics, this hybrid technique performed better at properly anticipating GHI. The results imply that more accurate and consistent GHI predictions can be achieved by utilizing the advantages of convolutional neural networks in combination with wavelet transforms.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
