Novel Machine Learning for Forecasting PV output

The rapid proliferation of wind and solar installations, coupled with the growing impact of climate change on the volatile UK weather, creates formidable operable challenges for the UK electricity Grid. To balance the supply and demand of the electricity system, the National Grid is required to take proactive actions to curtail variable renewable energy (VRE) generation. For example, the UK wasted over £274m (equivalent to 3.7 TWh) worth of VRE in 2020 alone, and this is projected to exceed £600m (~8 TWh) in 2030. Energy storage is often seen as a solution to this problem, however state-of-the-art solutions suffer from low round-trip efficiency (30%-80%), with the most widely used hydrogen storage option being only 30% efficient.

This project aims to unify multiscale machine learning and unconventional solar forecasting approaches to help balance demand and supply. The unifying approach will integrate and widen the ability of distributed or federated machine learning algorithms to be used on low-memory smart home user devices for optimised local solar predictions for smart energy management at more granular levels. With the ambitious use of distributed Edge-based machine learning and solar geometry instead of weather dependent solar irradiance, the multi-scale forecasting approach will produce a high-frequency capacity factor as the solar output multiple days and weeks ahead. Success in the approach could transform the prediction accuracy using future weather forecasting systems.

Academic qualifications

A second class honour degree or equivalent qualification in Computing, or Engineering

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr. Zuansi Cai (Email: [email protected]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.