A Hybrid Approach for the Ensembles of Neural Networks for Solar Power Forecasting
DOI:
https://doi.org/10.48001/joeeed.2024.221-4Keywords:
Convolutional Neural Networks (CNNs), Ensemble Neural Networks, Hybrid forecasting model, Renewable energy, Solar power forecasting, Sustainable energyAbstract
In this study, a unique hybrid method to solar power forecasting is presented. This strategy makes use of ensemble approaches, which include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs). A more accurate prediction of solar power production may be achieved using the hybrid ensemble, which takes advantage of the specific characteristics of each neural network design. Extensive trials conducted using datasets taken from the actual world demonstrate that the suggested technique is superior to both individual neural network models and conventional methods of predicting. Therefore, the ensemble is a feasible option for practical solar power forecasting applications, helping to the effective utilisation of renewable energy resources. Its flexibility to changing weather conditions, interpretability, and superior accuracy position it as a promising solution.
Downloads
References
Alshabandar, R., Hussain, A., Keight, R., & Khan, W. (2020, July). Students performance prediction in online courses using machine learning algorithms. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207196.
Angus, D. C. (2020). Randomized clinical trials of artificial intelligence. Jama, 323(11), 1043-1045.
https://doi.org/10.1001/jama.2020.1039.
Pandian , R., Vedanarayanan, V., Kumar, D. R., & Rajakumar, R. (2022). Detection and classification of lung cancer using CNN and Google net. Measurement: Sensors, 24, 100588.
https://doi.org/10.1016/j.measen.2022.100588.
Shuaib, M., Daud, S. M., Alam, S., & Khan, W. Z. (2020). Blockchain-based framework for secure and reliable land registry system. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(5), 2560-2571. http://doi.org/10.12928/telkomnika.v18i5.15787.
Sitaula, C., Grooby, E., Kwok, T. N. C., Sharkey, D., Marzbanrad, F., & Malhotra, A. (2023). Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: Artificial intelligence. Pediatric Research, 93(2), 426-436.
https://doi.org/10.1038/s41390-022-02417-w.
Takale, D. G., Mahalle, P. N., Sakhare, S. R., Gawali, P. P., Deshmukh, G., Khan, V., ... & Maral, V. B. (2023, August). Analysis of clinical decision support system in healthcare industry using machine learning approach. In International Conference on ICT for Sustainable Development (pp. 571-587). Singapore: Springer Nature Singapore.
https://doi.org/10.1007/978-981-99-5652-4_51.
Than, N. G., & Papp, Z. (2017). Ethical issues in genetic counseling. Best Practice & Research Clinical Obstetrics & Gynaecology, 43, 32-49.
https://doi.org/10.1016/j.bpobgyn.2017.01.005.
Wei, J. T., Barocas, D., Carlsson, S., Coakley, F., Eggener, S., Etzioni, R., ... & Lin, D. W. (2023). Early detection of prostate cancer: AUA/SUO guideline part I: Prostate cancer screening. The Journal of Urology, 210(1), 46-53. https://doi.org/10.1097/JU.0000000000003491.
