Supervisor
David McQuaid
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study investigates statistical and machine learning models for forecasting solar power generation in microgrids, focusing on the solar installation at Powell-Focht Bioengineering Hall, UC San Diego. Accurate predictions are critical due to the variability of solar energy, aiming to optimise microgrid operations and solar power efficiency. The research compares the performance of SARIMAX, LSTM, Random Forest, and ANN models using meteorological and solar power time series data. It finds that current meteorological inputs, especially solar radiation, enhance short-term forecasting accuracy over reliance on historical patterns.
The Random Forest Auto Regressor (RFAR) outperformed other models in 10-day-ahead solar power forecasting, followed closely by SARIMAX. The study highlights the practical benefits of combining clear-sky radiation with ground-level solar radiation to mitigate meteorological effects, offering valuable insights for microgrid energy management. Future directions include integrating these models into real-time control systems and leveraging advanced weather prediction technologies to further improve accuracy.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Dillon, Conor, "Statistical and Machine Learning Techniques for Predicting Solar Power Generation in a Microgrid." (2024). ICT. 67.
https://arc.cct.ie/ict/67