| Autor: |
Sharath, B. N., Madhu, K. S., Pradeep, D. G., Madhu, P., Premkumar, B. G., Karthik, S. |
| Zdroj: |
International Journal of Energy and Water Resources; 20240101, Issue: Preprints p1-7, 7p |
| Abstrakt: |
The probabilistic and unpredictable character of solar and wind power poses substantial hurdles to the dependable, economic and secure functioning of electrical energy systems as they are increasingly relied upon as the globe moves towards a more sustainable and renewable energy future. By utilising a solar tracker with a single axis, the research intends to improve the output of power generated by solar–wind hybrid production systems. Using a single-axis solar tracking system in conjunction with a solar panel is helping to boost the system's solar energy conversion efficiency. Solar–wind hybrid power generating system can deliver a continuous supply of electricity to meet the load requirement. The disadvantages imposed by poor climatic circumstances can be evaded by utilizing hybrid systems that harness energy from nature. Wind energy, which is also subject to fluctuations in wind speed, is unable to provide a constant output. So, a hybrid power generation employing renewable energy is more efficient for a constant balanced supply of electricity. It is consequently critical to enhance solar and wind power forecast accuracy to prepare for unknown future scenarios. The artificial neural network was used to predicting significance of the experiments and attributes of a variety of solar and wind prediction models. This research can assist scientists and engineers in analytically and experimentally analysing the attributes of a variety of solar and wind prediction models, thereby assisting them in selecting the model that is most suited for use in any given application situation. |
| Databáze: |
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