Report for Optimizing Renewable Energy Management through Solar Power Forecasting

Chockalingam Swaminathan, Karthikram (2024) Report for Optimizing Renewable Energy Management through Solar Power Forecasting. Masters thesis, Dublin, National College of Ireland.

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Abstract

With the shift towards renewable energy has increased exponentially, improving the grid stability, encouraging the transition navigating towards renewable energy sources and an efficiently predicting model for solar power are important for enhancing the management of renewable energy. This research study aims to use deep learning base approach specifically, Transformers for learning the complex patterns and trends in solar time series data. We also compared transformers based approach with various machine learning approaches like the Decision Tree Regression model, KNN (K-Nearest Neighbors) Regression model, Gradient Boosting Regression model. Transformer model has shown exceptional performance compared to other models, where the values of R², MSE, and MAE are 0.99999, 0.97, and 0.56 respectively while other machine learning approaches also performed better. Transformer model able to make better predictions because it able to learn the long range dependencies of the solar data as it is time series data. The transformer model has shown significant enhancements in terms of identifying dependencies and complex patterns present in the data, this helps in improved predicting accuracy. This research study aims is understand and show the capabilities of enhanced deep learning and machine learning models for improving the predicting accuracy of solar power. The knowledge is acquired to contribute enhancements in grid integration and energy storage management. Future works can concentrate on working with real-time data to improve prediction accuracy and develop more integrated models. This study contributes to reliable and sustainable energy systems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HC Economic History and Conditions > Natural resources H Social Sciences > HC Economic History and Conditions > Natural resources > Power resources
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	15 Aug 2025 16:58
Last Modified:	15 Aug 2025 17:03
URI:	https://norma.ncirl.ie/id/eprint/8546

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