Calum Dougan (2224080)
How can machine learning be used to efficiently analysis green energy in the forms of operational costs, carbon intensity, and which forms are most efficient within the UK region?
Project Abstract
With green energy on the rise over the past few decades, and its continued growth, making the transition towards green energy easier for the average person is more important than ever. To help with that I am aiming to provide easy to understand and accessible information as I believe this is the way to proceed. The neural network models I am producing are trained on publicly available weather and energy generation data and used synthetic data where some information was lacking. After the training of the models is complete and with a small bit of user information such as initial investments, projected operation time etc., the user will be able to produce easy to digest informative graphs that can show information like the projected efficiency and profitability of a selected green energy producer. These data visualisations are the main product and will hopefully provide the capability to help people make better informed decisions on making the transition to green energy sources.
Keywords: Machine Learning, Neural Networks, Data visualisations
Conference Details
Session: A
Location: Sir Stanley Clarke Auditorium at 11:00 13:00
Markers: Xianghua Xie, Adam Wyner
Course: BSc Computer Science 3yr FT
Future Plans: Other