PREDICTION OF SOLAR ENERGY PRODUCTION USING REGRESSION TECHNIQUES
| dc.contributor.author | THASHEELA A/P MURGIS | |
| dc.date.accessioned | 2026-04-27T02:12:34Z | |
| dc.date.issued | 2024 | |
| dc.description | Accurate solar energy production forecasting is essential for optimizing energy usage, storage, and integration with the power grid. This project leverages machine learning regression techniques to develop a predictive model using weather data such as temperature, humidity, cloud cover, and wind speed. Historical and real-time weather data are processed and analyzed to train and evaluate various regression models, including Linear Regression, Random Forest, and Support Vector Regression, based on metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The Knowledge Discovery in Databases (KDD) methodology guides the project's structured approach, encompassing data selection, pre-processing, transformation, and modeling. This study identifies key weather attributes influencing solar energy production and demonstrates the application of machine learning to provide reliable predictions. The findings contribute to the broader adoption of solar energy by enhancing predictability, reducing dependency on fossil fuels, and supporting global sustainability efforts. The project's outcomes aim to empower users with actionable insights for efficient energy management and decision-making. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/483 | |
| dc.language.iso | English | |
| dc.publisher | UNIVERSITI MALAYSIA SARAWAK | |
| dc.relation.ispartofseries | Faculty of Computer Science and Information Technology | |
| dc.title | PREDICTION OF SOLAR ENERGY PRODUCTION USING REGRESSION TECHNIQUES | |
| dc.type | Final Year Project |