Renewable power generation and weather conditions

Shaik, Saida Hussain (2024) Renewable power generation and weather conditions. Masters thesis, Dublin, National College of Ireland.

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Abstract

Traditional methods in power prediction like linear regression or even simple machine learning models have struggled to handle the complexities in time series data. This study explores the prediction of solar power generation using two machine learning models: Random Forest and Long Short-Term Memory (LSTM) networks using a data set obtained from two solar power plants in India. Records in the dataset cover 34 days in total, during which, there are the power generation record per 15 minutes and the weather data or the ambient temperature, module temperature, and irradiation. The main purpose to estimate the TOTAL_YIELD of solar plant in respect of weather and power generation characteristics. The first transforming process is the data pre-processing in which data is cleaned and converted to a supervised form With the help of a feature extractor, data is divided into training and testing sets. For model implementation, the Random Forest Regressor is employed to predict the total yield and the LSTM model to analyze time series data. The performance of both models is assessed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and R² score. The outcomes reveal that the two models are reasonably accurate but the LSTM model provides better predictions with lesser error rates, which confirm the viability of time series forecasting. The final analysis is based on identifying the strengths of LSTM in terms of forecasting sequential data for renewable energy and, at the same time, the interpretability of Random Forest for features’ importance.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Subhnil, Shubham UNSPECIFIED
Uncontrolled Keywords:	Solar Power Generation; Renewable Energy; Time-Series Data; Machine Learning; Long Short-Term Memory (LSTM)
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences 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
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 14:36
Last Modified:	04 Sep 2025 14:36
URI:	https://norma.ncirl.ie/id/eprint/8800

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