View Proposal

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Proposer

Radu-Casian Mihailescu

Title

Solar Photovoltaic Characterisation & Yield Prediction

Goal

Description

Renewable Energy is generally capital intensive relative to its cost of operation and maintenance. The cost of raising capital therefore has a significant impact on the Levelised Cost of Energy (LCOE). Higher confidence of yield prediction throughout the expected life of a renewable energy plan therefore can drive down the cost of energy. This project will train artificial neural networks to better predict energy yield in varying environmental conditions (irradiance, air mass, ambient and panel temperatures, etc) and over long periods of time, to account for cleaning schedules and degradation rates.

Resources

Background

Url

Difficulty Level

Easy

Ethical Approval

None

Number Of Students

1

Supervisor

Radu-Casian Mihailescu

Keywords

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Bachelor of Science in Information Systems
Bachelor of Science in Software Development for Business (GA)
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Business Information Management
Master of Science in Computer Science for Cyber Security
Master of Science in Computer Systems Management
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Information Technology (Business)
Master of Science in Information Technology (Software Systems)
Master of Science in Network Security
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics
Bachelor of Science in Computer Science (Cyber Security)
Master of Science in Robotics with Industrial Application
Postgraduate Diploma in Artificial Intelligence