Solar Energy Prediction using PSO_SVR - Case study Adrar site -
| dc.contributor.author | MAHDID Sabah | |
| dc.contributor.author | ENCA/ Mezaache Hatem | |
| dc.date.accessioned | 2025-07-16T07:43:34Z | |
| dc.date.available | 2025-07-16T07:43:34Z | |
| dc.date.issued | 2025-06-30 | |
| dc.description.abstract | The integration of renewable energy sources into the energy mix is crucial to the transition to a low-carbon economy. Among these sources, solar energy occupies an important place due to its availability and potential to provide clean, renewable energy. However, the intermittent and variable nature of solar power generation creates major challenges for the management and stability of power grids. This work proposes an innovative approach for estimating solar power over different time horizons, combining advanced optimization and machine learning techniques. Particle swarm optimization (PSO) is used to efficiently adjust the hyper-parameters of support vector regression (SVR), thereby improving the accuracy of the estimates. This method better captures variations in solar production as a function of meteorological and temporal conditions, offering a powerful and adaptable solution for energy forecasting applications. To evaluate our estimation system two evaluation methods are employed, a statistical evaluation based on performance indicators such as root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), as well as a graphical evaluation using scatter plots to compare actual data with predicted results, these evaluation methods allowing to compare predictions with actual values and to verify the effectiveness of the proposed hybrid approach. | |
| dc.identifier.other | EL/24/25 | |
| dc.identifier.uri | https://repository.univ-msila.dz/handle/123456789/47253 | |
| dc.language.iso | en | |
| dc.publisher | University of M'sila | |
| dc.subject | Estimation | |
| dc.subject | Solar power | |
| dc.subject | meteorological inputs | |
| dc.subject | PSO | |
| dc.subject | SVR. | |
| dc.title | Solar Energy Prediction using PSO_SVR - Case study Adrar site - | |
| dc.type | Thesis |