Real-time forecasting PV output using novel Machine Learning

About the Project

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 about £400m (equivalent to 8.3 TWh) worth of VRE in 2024 alone, and this is projected to exceed £1 bn 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 dependant 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.

Perspective applicants are encouraged to contact the Supervisor before submitting their applications. Applications should make it clear the project you are applying for and the name of the supervisors.

Academic qualifications

First degree (minimum 2:1 classification) in computing, mathematics or Engineering with a good fundamental knowledge of renewable energy

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Good fundamental knowledge of computing and mathematics
• Competent in programming and data analysis
• Knowledge of renewable energy system
• Good written and oral communication skills
• Strong motivation with evidence of independent research skills relevant to the project

Desirable attributes:

• Some experience in machine learning

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The 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.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. 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.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact z.cai@napier.ac.uk

PhD Start Date: October 2026

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?mP9MDnTs1Rwm8ftb3WVhDhXtraMQwXSUMdHC9wIc34es5bJqXf

Funding Notes

International applicants should note that visa application costs and the NHS health surcharge are additional costs to be taken into consideration, and successful applicants will need to cover these expenses themselves.