| dc.contributor.author | Azra, Azyuari | |
| dc.date.accessioned | 2021-08-27T13:03:40Z | |
| dc.date.available | 2021-08-27T13:03:40Z | |
| dc.date.issued | 2021-08-25 | |
| dc.identifier.uri | https://library.universitaspertamina.ac.id//xmlui/handle/123456789/4205 | |
| dc.description.abstract | The utilization of renewable energy to diminish the impacts of global warming and climate change has become an increasing innovation. The need for green energy has led to increased focus on related to solar irradiance utilization. The availability of reliable solar irradiance data is essential factor in the success of energy installation in certain locations. However, not every location has the availability of solar irradiance. The study aims to analyze the best method to forecast solar irradiance based on multiple machine learning methods, Multi Layer Perceptron, Random Forest Regression, and Support Vector Regression. The
performance of the proposed models was evaluated using meteorological dataset in three different location. The result revealed that RFR is the most accurate approach compared with MLP and SVR. The purposed solar irradiance forecasting model is
applied to solar energy in Baubau, Indonesia. | en_US |
| dc.language.iso | other | en_US |
| dc.subject | Solar Irradiance Forecasting, Machine Learning, Supervised Learning, Multi Layer Perceptron, Random Forest Regressor, Support Vector Regression | en_US |
| dc.title | PERAMALAN RADIASI SURYA MENGGUNAKAN METODE MACHINE LEARNING UNTUK PERENCANAAN PRODUKSI ENERGI PANEL SURYA | en_US |
| dc.title.alternative | SOLAR IRRADIANCE FORECASTING USING MACHINE LEARNING METHOD FOR SOLAR PANEL ENERGY PRODUCTION PLANNING | en_US |
| dc.type | Thesis | en_US |
