| Issue |
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
|
|
|---|---|---|
| Article Number | 02011 | |
| Number of page(s) | 12 | |
| Section | Renewable Energy & Electrical Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202340502011 | |
| Published online | 26 July 2023 | |
Short term solar irradiation forecasting using Deep neural network with decomposition methods and optimized by grid search algorithm
1 Department of Mechatronics Engineering, Chandigarh University, Mohali, India
2 Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India
* Corresponding author: rijulsrivastva@gmail.com
Due to the variable nature of solar energy, it is necessary to manage a bilateral contract negotiation between suppliers and customers. Therefore, to fulfil this condition, this paper proposed an ensemble approach to forecast the solar irradiation. The signal processing techniques Variational Mode Decomposition (VMD) and Discrete Wavelet Transform (DWT) used with deep neural network to forecast the solar irradiation. The hyperparameters of deep learning model are optimized using grid search optimization with in a suitable tolerable search range. The data of three years (2012-14) is used; where data of year 2012-2013 is used to train model and testing is done on data of year 2014 for New Delhi location. Among all developed models, Bi-LSTM-VMD-Grid Search performance is better in terms of RMSE (5.456W/m2), MAPE (0.948%) and R2(0.924%), Because Bi-LSTM process the information twice and faster than other algorithms and VMD refine the quality of input data better as comparison to DWT. The result of proposed model is compared with existing techniques that predicted the solar irradiation and the forecasted results are more efficient and reliable.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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