Solar Sight Forecast: Deep Learning Approaches for Solar PV Power Prediction at Bui Solar Power Station Ghana

Sopte, Mitali (2024) Solar Sight Forecast: Deep Learning Approaches for Solar PV Power Prediction at Bui Solar Power Station Ghana. Masters thesis, Dublin, National College of Ireland.

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Abstract

The major crisis faced by the BUI Power Authority was to cope with the consistent distribution and generation of solar energy, which is influenced by various climatic conditions like humidity, wind, ambient temperature, global irradiation, etc. This research aims to enhance the understanding of solar power generation and enable reliable energy distribution to the organisation. The previous research that deployed machine learning models like gradient boosting and random forest achieved an accuracy of 90% and a normalised mean absolute error of 1.18%. Based on these findings, the approach to understanding how a deep learning model like LSTM can be used to increase the accuracy and overall outcome was carried out in this study. These findings can result in the potential of deep learning techniques, which can help in assisting the BUI Power Authority in utilising energy appropriately. The implication of this research is to enhance the reliability of solar energy supply, resulting in the broader goal of sustainability of renewable resources.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Singh, Jaswinder UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Energy industries Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HC Economic History and Conditions > Natural resources
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 Aug 2025 10:53
Last Modified:	26 Aug 2025 10:53
URI:	https://norma.ncirl.ie/id/eprint/8635

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