| dc.contributor.author | Wolimbwa, Gadenya Norman | |
| dc.date.accessioned | 2021-05-04T07:44:20Z | |
| dc.date.available | 2021-05-04T07:44:20Z | |
| dc.date.issued | 2020-12-21 | |
| dc.identifier.citation | Wolimbwa, G.N. (2020). 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 (Unpublished undergraduate dissertation) Makerere University.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/20.500.12281/10523 | |
| dc.description | A dissertation submitted in partial fulfilment of the requirements for the award of the Bachelor of Science in
Computer Engineer degree of Makerere University. | en_US |
| dc.description.abstract | The purpose of this study was to assess the efficiencies of solar PV panels.
I was able to undertake this by training a machine learning model using solar irradiance , wind speed, ambient temperature and panel manufacturer as the independent variables from which predicted the efficiencies for the respective PV panels. The model was that was trained was about 99\% which was able to predict efficiencies of three different solar panels based on the temperature, irradiance, wind speed and the panel manufacturer. However, the model would
have been more practical if the data set used was wide enough to account for climatic change and if less assumptions on the panels were made during model development. I were also able to deploy the model for use in a web application. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Machine learning model | en_US |
| dc.subject | PV Panel 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 |
