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​Artificial Intelligence for Energy Efficiency and Emission Reduction Toward Net Zero​

Doctoral Training Grant Funding Information 

This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.    

All applicants will receive the same stipend irrespective of fee status. 

Application Closing Date: 


Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.  

How to Apply 

To apply, please follow the below steps:  

  1. Complete the BCU Online Application Form. 
  2. Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.  
  3. Upload two references to your online application form (at least one of which must be an academic reference). 
  4. Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s). 
  5. International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores. 

Frequently Asked Questions 

To help support you to complete your application, please consult the frequently asked questions below: 

Project title: ​​Artificial Intelligence for Energy Efficiency and Emission Reduction Toward Net Zero​ 

Project Lead: ​​Associate Professor Ogerta Elezaj​ 

Project ID: ​​​35 - 45490135​​ 

Project Description: 

​​This PhD project focuses on harnessing artificial intelligence (AI) to improve household energy efficiency and reduce carbon emissions, key challenges in achieving a sustainable, Net Zero future. The research investigates how advanced AI techniques, especially deep reinforcement learning (DRL), can be leveraged to enhance residential energy management, facilitate greater use of renewable energy, and reduce both carbon output and peak electricity demand.

​The project will develop intelligent forecasting models to predict electricity prices, household energy consumption, grid carbon intensity, and renewable energy generation (e.g., from photovoltaic systems). These forecasts will feed into optimisation algorithms designed to schedule and coordinate energy usage and storage. The aim is to shift electricity consumption to times when energy is both cheaper and cleaner, helping households cut costs and lower their environmental impact.

​A key emphasis will be on applying DRL to model and influence household energy decisions under changing electricity tariffs, carbon signals, and grid limitations. DRL provides a powerful framework for managing real-time, high-dimensional control problems. The research will explore how DRL agents can autonomously and adaptively manage home energy assets, including batteries, smart appliances, and solar panels based on Time-of-Use pricing and demand response signals.

​In addition to the technical innovation, the project will address the social and behavioural dimensions of AI-driven energy management. It will identify the factors that enable or hinder household participation in demand response programs, which hold great promise for grid flexibility but are still underused.

​The anticipated outcomes include a set of AI-powered predictive and optimisation tools, along with a DRL-based control framework for intelligent household energy management. These contributions will help inform future energy policy, support technology development, and promote fair and sustainable energy transitions.

​Overall, this PhD sits at the intersection of AI, energy systems, and human behaviour, offering valuable insights for the development of smart, low-carbon homes and contributing to the broader effort to decarbonise the built environment.

Anticipated Findings and Contribution to Knowledge:

​​This research will generate novel insights into the role of artificial intelligence (AI) in optimising residential energy management, with a particular focus on the integration of renewable energy sources, energy storage technologies, and demand response (DR) mechanisms. By leveraging AI for real-time prediction and control, the study aims to empower households to make smarter, more sustainable energy decisions—reducing both operational costs and environmental impact.

​The anticipated findings will deepen understanding of how AI can be used to forecast electricity prices, CO₂ emissions, and renewable energy availability, enabling more intelligent and adaptive energy usage. This work will advance the field through several key contributions:

  1. ​AI-Driven Optimisation Models: Development of advanced deep reinforcement learning (DRL) algorithms capable of optimising the timing and coordination of household energy consumption, storage, and solar power usage. These models will enhance energy efficiency and reduce electricity costs by aligning consumption with favourable pricing and carbon intensity conditions.
  2. ​Integrated Energy Storage and PV Solutions: Exploration of novel strategies for integrating multi-level storage systems—including thermal and battery storage—with residential photovoltaic (PV) systems. This integration will improve renewable energy self-consumption and reduce dependence on the central grid.
  3. ​AI-Guided Demand Response and Emissions Reduction: Investigation into how AI-optimised demand response strategies can increase the share of zero-carbon electricity in household energy use, offering a practical route to reducing residential carbon emissions.
  4. ​Conversational AI-Enabled Energy Management Systems (EMS): Evaluation of the feasibility and impact of incorporating conversational AI into EMS interfaces. This human-centric approach aims to enhance user engagement and promote energy-conscious behaviours, addressing a critical gap in existing research.

​Together, these contributions will advance the design and implementation of intelligent, user-adaptive residential energy systems. The outcomes will inform policy, support the development of next-generation EMS solutions, and contribute meaningfully to the broader goal of achieving Net Zero.

Person specification:

Entry Requirements: 

  • To apply for our Computing PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.  

  • Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.  

  • We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.  

 Essential Criteria: 

  • Analytical thinking and the ability to work independently as well as collaboratively in interdisciplinary teams. 

  • Strong written and oral communication skills in English. 

  • Programming proficiency in Python, with experience using machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn. 

  • A strong understanding of Artificial Intelligence and Machine Learning methods, particularly deep learning and reinforcement learning. 

  • Experience with time-series forecasting, control optimisation, or energy load modelling.  

Desirable Criteria: 

  • ​​2–3 years of experience in full-stack machine learning engineering is highly desirable. 

  • ​Familiarity with the built environment or residential energy domain applications. 

  • ​Experience working with Linked Building Data or open energy datasets. 

  • ​Knowledge of knowledge graphs and semantic web technologies. 

  • ​Familiarity with Natural Language Processing libraries in Python (SpaCy, Scikit-learn, NLTK), particularly relevant if Conversational AI features are explored. 

  • ​Publications in peer-reviewed, high-impact journals or conferences in AI, energy informatics, or smart systems. 

Overseas Applicants:

International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.

Contact:

If you have any questions or need further information, please use the contact details below:

For enquiries about the project content, please contact: ogerta.elezaj@bcu.ac.uk​

For enquiries about the application process, please contact: research.admissions@bcu.ac.uk