Forecasting PV-Diesel Microgrid Campus Load Using Machine Learning: The University of the Free State QwaQwa Campus Microgrid

Abstract

This thesis focused on using machine learning techniques to forecast the load of a photovoltaic (PV)-diesel-powered microgrid on the QwaQwa Campus at the University of Free State in South Africa. The hybrid PV-diesel system is installed to mitigate frequent power shortages faced in South Africa. The methodology involved training multiple ML models, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), hybrid Convolutional Neural Networks (CNN), Extreme Gradient Boosting (XGB), and a Random Forest (RF) Regressor, using historical consumption data. These models were evaluated based on their performance in accurately predicting short-term electricity consumption.

This thesis focused on using Machine Learning (ML) techniques to forecast the load of a photovoltaic (PV)-diesel-powered microgrid on the QwaQwa Campus at the University of Free State in South Africa. The hybrid PV-diesel system is installed to mitigate frequent power shortages faced in South Africa. The methodology involved training multiple ML models, including Long Short-Term Memory networks, Gated Recurrent Units, hybrid Convolutional Neural Networks, Extreme Gradient Boosting, and a Random Forest Regressor, using historical consumption data. These models were evaluated based on their performance in accurately predicting short-term electricity consumption.