@article{4070,
  author = {Myroslav Komar, Khrystyna Lipianina-Honcharenko, Valentyn Domanskyi, Nazar Melnyk},
  title = {The Gradient Boosting Regressor efficient integration of solar energy in Grid Systems},
  journal = {Transactions on Machine Design},
  year = {2024},
  volume = {12},
  number = {2},
  doi = {https://doi.org/10.6025/tmd/2024/12/2/50-61},
  url = {https://www.dline.info/tmd/fulltext/v12n2/tmdv12n2_1.pdf},
  abstract = {The significance of this study is rooted in the increasing dependence on solar energy as a primary source of renewable power. Solar energy facilities provide cost-effective operation, simple upkeep, and a high level of dependability, making them a compelling choice for the generation of clean energy. Nonetheless, the performance of these facilities can be greatly affected by outside elements such as climate conditions and the physical attributes of the solar panels. This document explores various forecasting periods, from immediate to extended, and examines the appropriateness of various models, including artificial neural networks, time-series forecasting, machine learning, and combination approaches, for these purposes. By analyzing data from a solar energy facility, the research evaluates multiple regression models to determine the most accurate one. The Gradient Boosting Regressor was found to be the most effective, showcasing its capability in precisely predicting solar energy production. The success of this approach highlights the potential for more efficient integration of solar energy facilities into smart grid systems and the enhancement of energy management strategies. The study introduces a reliable method for real-time forecasting of solar energy facility performance, which could greatly improve energy management and the incorporation of renewable energy sources into power grids. It paves the way for further research on enhancing forecasting methods and emphasizes the importance of precise prediction models in the development of renewable energy technologies.},
}