Physics-informed machine learning for renewable energy forecasting

Overview

Accurate forecasting is at the heart of many modern industries, from energy and transport to retail, supply chains, finance, climate, and health. This research project explores deep learning approaches for (renewable) energy forecasting, investigating how modern architectures such as recurrent neural networks, LSTMs, and multimodal foundation models can shape the next generation of forecasting systems.

The overarching goal is to develop robust, interpretable, and scalable energy forecasting models that outperform classical methods and work effectively in real-world settings, including domains with partial information, high uncertainty, and rapidly changing environments.

This project suits students who enjoy machine learning, time series, programming, and applied research with potential impact across multiple industries.

Research engagement

You will be part of the Forecasting Insights and Decision Making Group at QUT. Students will work on a structured set of research activities, tailoring their focus to  energy.

Research activities

• survey deep learning models for forecasting
• identify gaps in existing research
• work with real or simulated datasets (energy wind/solar)
• perform time-series cleaning, scaling, feature engineering, and handling missing data
• build pipelines for training/validation/testing and rolling-origin evaluation.

Research skills

World-class expertise in forecasting and cutting-edge skills in machine learning, and understanding research

Outcomes

The expected outcomes are:

• a deep learning forecasting model
  • designed, trained, and evaluated on real-world or simulated data
• a technical research report
  • documenting methods, experiments, and insights
• a publishable paper
  • a short manuscript suitable for submission to a forecasting or applied ML venue.

Skills and experience

Strong Python Programming, Familiar with Machine Learning and Deep Learning

Start date

2 November, 2026

End date

19 February, 2027

Location

Online and In person QUT

Keywords

• Energy
• Forecasting
• Machine Learning

Contact

Mahdi Abolghasemi

07 3138 0393

mahdi.abolghasemi@qut.edu.au