| dc.contributor.author | Ejulu, Wilson | |
| dc.date.accessioned | 2022-05-19T12:21:30Z | |
| dc.date.available | 2022-05-19T12:21:30Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | Ejulu, Wilson. (2021). Development of a machine learning model for the assessment of PV panel efficiency. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/12785 | |
| dc.description | A final year project report submitted to the College of Engineering Design and Art in partial fulfillment of the requirement for the award of the degree Bachelor of Science in Computer Engineering of Makerere University. | en_US |
| dc.description.abstract | The purpose of this study was to assess the efficiencies of solar PV panels. We were able to
undertake this by training a machine learning model using solar irradiance, windspeed, ambient
temperature and panel manufacturer as the independent variables from which we predicted the
efficiencies for the respective PV panels. The model was able to perform well based on the used
dataset. However, the model would have been more practical if the dataset used was wide enough
to account for climatic change and if less assumptions on the panels were made during model
development. We were also able to deploy the model for use in a web application | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Photovoltaic system | en_US |
| dc.subject | Efficiency | en_US |
| dc.title | Development of a machine learning model for the assessment of PV panel efficiency. | en_US |
| dc.type | Thesis | en_US |
