| Issue |
E3S Web Conf.
Volume 475, 2024
InCASST 2023 - The 1st International Conference on Applied Sciences and Smart Technologies
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 12 | |
| Section | Renewable Energy Technologies and Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202447503009 | |
| Published online | 08 January 2024 | |
Machine learning based modeling for estimating solar power generation
1 Department of Informatics, Universitas Pembangunan Jaya, Tangerang Selatan, Indonesia
2 Department of Management, Universitas Pembangunan Jaya, Tangerang Selatan, Indonesia
3 Department of Product Design, Universitas Pembangunan Jaya, Tangerang Selatan, Indonesia
4 Center for Urban Studies, Universitas Pembangunan Jaya, Tangerang Selatan, Indonesia
* Corresponding author: nur.uddin@upj.ac.id
The solar power plant is a rapidly growing renewable energy source that has a potential role in reducing climate change and replacing fossil fuels. Estimation of the power generated by a solar power plant is required to determine the energy supply. Unfortunately, the solar power generated is highly uncertain due to highly dependence to nature, such as solar radiation and weather. This makes the estimation of solar power generation to be very difficult. This study presents a development of machine learning to model a solar power plant for estimating the generated power. The machine learning is developed by implementing the k-NN algorithm. A data set of power generated in a solar power plant is applied to build the machine learning. The development resulted in a machine learning that models the solar power plant. Simulation test result show the machine learning was able to estimate the solar power generated with an accuracy of 69.6%. The developed model is very useful to estimate potential of solar power resource in a region. The developed model is very useful in feasibility studies to estimate the potential of solar power resources in an area.
© The Authors, published by EDP Sciences, 2024
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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