View Proposal

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Proposer

Usman S Sanusi

Title

State-of-the-art Machine learning in Energy demands and supplies

Goal

Description

This project aims to develop Machine Learning (ML) models for predicting energy demands and supplies. Features include energy generation and consumption profiles, historical weather variables (wind speed, temperature, humidity) time (hours, days, weekly, seasons) and sundry indices. Energy forecasting methods involve techniques that are either purely time series or hybrid models, which can be univariate or multivariate. Time series models are mostly regarded as the simplest, they exploit trends in the time series to extrapolate future energy requirement using popular time series approaches. And more recently, advanced statistical and machine learning methods have been effectively utilized with remarkable successes.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Usman S Sanusi

Keywords

forecasting,artificial intelligence, machine learning, renewable and electrical energy

Degrees

Bachelor of Science in Computer Science
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Data Science
Master of Science in Information Technology (Software Systems)
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Science in Statistical Data Science
BSc Data Sciences