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The digital transformation of energy management is calling for the leaders of our decarbonised future - capitalise your career prospects in this exciting field with the Advanced Digital Energy Systems MSc

The future energy supply must adapt to new technologies, global policies and events while accounting for new emerging consumption patterns. Our MSc in Advanced Digital Energy Systems will allow you to apply techniques and technologies such as Artificial Intelligence and Blockchain in the energy field, supporting the transition to a more sustainable future. The need for an affordable, sustainable, and secure energy supply makes managing energy systems a complex problem. Digital energy systems are an emerging discipline that uses powerful digital tools and models to analyse data from our power systems and optimize the control and operational strategies and business models. 

You will acquire a comprehensive knowledge of data analytics and machine learning techniques applied to the integration of renewable energy, smart and microgrids, forecasting energy production and consumption, whilst supporting key objectives such as reaching Net Zero emissions.


  • Start dateFull-time: October. Part-time: October
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course is suitable for Electrical and Electronic Engineering, Computer Science, Mathematics, Engineering and Information Technology and Energy graduates and practicing IT or Energy engineers wishing to pursue a technical management career in the rapidly growing digital energy sector. It develops professional engineers and scientists with the multidisciplinary skills and ability to analyse current and future energy engineering challenges.

Your career

The international nature of this growing field allows Cranfield graduates to develop diverse and rewarding global careers in industry, government or research.

Example careers:

  • Energy Analyst – data science,
  • Offshore Energy Analyst,
  • Energy and Sustainability Analyst,
  • Research Analyst - Energy.

Cranfield Careers and Employability Service

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

Why this course?

Developed economies now face challenges in procuring energy security and responding to energy pricing and affordability issues, as well as contributing to reducing carbon emissions such as the UK Government’s ambitious targets of reducing greenhouse gas emissions to close to zero in the power sector by 2050.

You will benefit from dedicated state-of-the-art facilities including unique engineering-scale facilities for the development of efficient technologies with low CO2 emissions. In addition to management, communication, teamwork and research skills, each student will attain at least the following learning outcomes from this degree course:

  • Design an appropriate data acquisition system for energy related processes,
  • Critically analyse industrial data collected from different energy systems,
  • Develop systematic strategies using a range of software for energy systems modelling, optimisation and control to resolve the technical issues involved in the design and operation of industrial energy systems.
This MSc is supported by our team of professorial thought leaders, including Professor Nazmiye Ozkan, who is influential in the field of digital energy, and an integral part of this MSc.
My group project was actually with an industry, one of the leading industries in the renewable energy investment sector. So we worked with them as a consultant – so it was like working in industry, not just purely academic.
Data and data skills are really important to the energy sector as it's developing and transitioning. AI and blockchain technologies are really important to that transition but it's really important that we have the right skills and the technologies to process that data and use it accordingly.
AI blockchain in the energy sector is about that user experience. It's about taking that user centric design, place for place and understanding how we can shift that mass market and then how we can guarantee the experience that is a commercially viable for that organisation and doesn't degrade the experience that they currently have with a centralised generation system.
I think energy is one of our key challenges we face as a society. If we don't address the energy challenge, if we don't decarbonise our energy systems which make up a big chunk of our CO2 production, at the moment all else probably won't matter. Why AI and blockchain? Because I think they are two of the key technologies which are going to help us address those now there are lots of other technologies as well there are things like the Internet of Things. I think there's still a lot of work to be done on other social sciences and psychology of how to change people's behaviour because ultimately all of these technologies only make a difference if people decide to use them if people act on the advice – for example, given by an AI system – if people trust to hand over the controls to an automated system.

Informed by Industry

We have a world class reputation for our industrial-scale research and pilot-scale demonstration programmes in the energy sector. Close engagement with the energy and transport sectors over the last 20 years has produced long-standing strategic partnerships with the sectors’ most prominent players. The strategic links with industry ensure that all of the material taught in the course is relevant, timely and meets the needs of organisations operating within the energy sector. This industry-led education makes our graduates some of the most desirable in the world for energy companies to recruit.

Course details

The taught programme for the Advanced Digital Energy Systems masters is generally delivered from October to February and is comprised of eight modules. Each of the first five modules are delivered over two weeks. Generally the first week involves intensive teaching while the second week has fewer teaching hours to allow time for more independent learning and completion of the assessment.

Water course structure diagram

Students on the part-time programme will complete all of the modules based on a flexible schedule that will be agreed with the course director.

Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%.

Group project

The group project is an applied, multidisciplinary, team-based activity. Often solving real-world, industry-based problems, you are provided with the opportunity to take responsibility for a consultancy-type project while working under academic supervision. Success is dependent on the integration of various activities and working within agreed objectives, deadlines and budgets. Transferable skills such as team work, self-reflection and clear communication are also developed.

Individual project

The individual project is the chance for you to focus on an area of particular interest to you and your future career. You will select the individual project in consultation with the Thesis Co-ordinator, your allocated supervisor and your Course Director. These projects provide you with the opportunity to demonstrate your ability to carry out independent research, think and work in an original way, contribute to knowledge, and overcome genuine challenges in the energy industry. Many of the projects are supported by external organisations.


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Renewable Energy Technologies 1

Module Leader
  • Dr Peter King
    An understanding of the principles of renewable energy technologies is key to assimilate the technological basis of the systems and applications. The module provides the fundamentals of the renewable energy technologies and their impact on global and national energy system. The purpose of this module is to introduce the basis for assessment of the performances of solar (both PV and CSP), wind, wave and tidal, geothermal as well as hydro-electricity technologies.  By the end of the module, you will have a better understanding of the various renewable technologies and will have the opportunity to visit a PV solar plant to see the real dimension of an operational plant. 
    • Photovoltaic technology,
    • Concentrated solar power technology,
    • Onshore and offshore wind energy: fundamentals of wind turbines and placement,
    • Geothermal Systems (including ground-source heat pumps),
    • Wave and tidal energy technologies,
    • Hydro-electricity technology and systems.
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Identify the different components and main configuration of the different renewable technologies covered in the module,
  2. Articulate the fundamental principles, terminology and key issues related to the most used renewable energy technologies,
  3. Critically compare the challenges for the development and operation of the major technologies, including government regulation and policy,
  4. Identify gaps in the knowledge and discuss potential opportunities for further development, including technology and economic potential.

Renewable Energy Technologies 2

Module Leader
  • Dr Jerry Luo
    This module provides detailed knowledge in energy storage, bioenergy, energy harvesting and energy distribution. This module also provides you with knowledge and experience in designing and analysing renewable energy infrastructures in energy storage, distribution and corresponding renewable energy applications.

    Energy storage materials and technologies

    • Electrochemical and battery energy storage,
    • Thermal energy storage,
    • Medium to large scale energy storage,
    • Hydrogen storage.


    • Biorefinery,
    • Biofuels.

    Energy distribution

    • Smart grid and micro-grid,
    • Smart grid for case study
    • Machine learning in energy.

    Energy harvesting

    • Energy harvesting technologies,
    • Case Study.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically evaluate the key benefits and challenges of energy storage, bioenergy, energy harvesting and distribution in renewable energy,
  • Identify the appropriate energy storage and distribution methods for different types of renewable energy systems,
  • Analyse the main configurations and components in energy storage and distribution for renewable energy systems,
  • Justify the importance of materials, control, integration and information management issues in renewable energy,
  • Appraise future technology and socio-economic trends in sustainability and assess associated opportunities and challenges.

Cybersecurity for Energy Systems

Module Leader
  • Dr Adam Zagorecki
    This module introduces the cyberspace aspects of digital energy systems. It will focus on threats, actors and exploitation of infrastructure. The module also covers security technologies available to support and protect digital energy systems, as well as security requirements and corresponding vulnerabilities.
    • Introduction to Cyber Security
      • Understanding cyberspace, cyber-crime, cyber-attack and cyber-war, 
      • The different categories of threat actors and their motivations  .
    • Attacks and Vulnerabilities 
      • An overview of common cyber-attacks, for example, SQL injection, XSS, and enumeration,
      • Explanation of how these attacks can be mitigated, including the use of penetration testing,
      • Understanding the human aspects of vulnerabilities, for example, insider threat and social engineering. 
    • Critical Infrastructure
      • Critical Infrastructure, defining criticality, global and national level view, organisational level view, supply chain perspective,
      • Critical Information Infrastructure SCADA, defining SCADA, role in critical infrastructure and processes,
      • Network monitoring, operations management, indicators and warnings, intrusion detection, penetration testing, the strategic context.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Knowledge
  • Assess cyber operations from a variety of threat actors,
  • Evaluate the different cyber vulnerabilities and how they might impact an organisation,
  • Appraise the strengths and weaknesses of various security technologies and their suitability for protecting an organisation.
  • Skills
  • Develop a security strategy using appropriate technologies and techniques,
  • Prioritise cyber threats and vulnerabilities based on their potential business impact.

Data Analytics for Energy Systems

Module Leader
  • Dr Chao Long
    This module will introduce you to data analytics, overview challenges and solutions in using data analytical tools in energy systems, present approaches to predictive and descriptive data mining, classification, statistical methods, regression models and explain unsupervised learning techniques suitable for new information discovery. Students may benefit from knowledge of basic concepts of statistics methods for performance assessment and evaluation, regression models (linear, non-linear, Gaussian, Bayesian Logistic), and classification methods.
    • Introduction to Data Analytics,
    • Statistics refresher and data pre-processing,
    • Predictive analytics: regression refresher and classification methods,
    • Clustering and dimensionality reduction,
    • Graph analysis and visualisation,
    • Software and tools for data analytic,
    • Case study: application of data pre-processing and data analytical tools for a specific dataset.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically analyse stages of the data analytics workflow; and establish a data analytics workflow based on the available data and formulated requirements,
  • Critically evaluate data analysis and visualisation techniques with respect to data analytics stages, using graph analysis and visualisation techniques,
  • Analyse and apply algorithms for discovery of new information from the large data sets, using statistics, regression, classification methods,
  • Evaluate performance of the algorithms and quality of the data analysis outcomes.

Artificial Intelligence for Energy Systems

Module Leader
  • Dr Da Huo
    With more and more measurement and control devices installed in energy systems, data analytics using AI technology to support planning and operation of energy systems has shown significant advantages. The scientific and technical concepts of machine learning and AI methods/tools and their potential advantages in the energy sector will be taught in this module. One example of this is to use smart metering data to analyse a network’s hosting capacity of solar photovoltaic systems, and to analyse a power system’s technical and non-technical energy losses.  The module aims to provide you with data analytical skills from machine learning and AI technology, and evaluate the advantages/disadvantages of their applications in the energy industry. The module also aims to provide you with essential skills (e.g. computer programming and coding in Python) for applying machine learning and AI in the energy industry.
    • Design of an appropriate analysis toolkit specific to analyse the examples of applications of machine learning and AI technology in energy industry,
    • Analysing the development and scaling/design of the AI technologies by evaluating the advantages/disadvantages of the available examples in the areas of applications in energy systems,
    • AI techniques (e.g. Artificial Neural Networks, RNN, Reinforcement Learning), and skills to manage, process and use data to support network operation and planning,
    • Techniques to evaluate where AI can be used and the potential benefits to the energy industry. 
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Critically analyse the state-of-the-art of the applications of machine learning (ML) and AI technology in the energy industry,
  2. Identify and assess the requirements of different AI/ML techniques and their contributions to improve the planning and operation of energy systems,
  3. Implement AI/ML algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study,
  4. Evaluate the advantages and disadvantages of particular AI techniques within the context of the energy industry.

Energy Systems Case Studies

Module Leader
  • Dr Renaldi Renaldi
    The module aims to provide you with a deep understanding of the truly multidisciplinary nature of a real industrial project.  Using a relevant case study, the scientific and technical concepts learned during the previous modules will be brought together and used to execute the analysis of the case study.
    • Work flow definition: setting up the single aspects to be considered, the logical order, and the interfaces.
    • Design of an appropriate analysis toolkit specific to the case study
    • Development of a management or maintenance framework for the case study
    • Multi-criteria decision analysis [MDCA] applied to energy technologies to identify the best available technology. 
    • Energy technologies and systems: understanding the development and scaling/design of the technologies by applying an understanding of the available resources in the assigned location;
    • Public engagement strategies and the planning process involved in developing energy technologies.
Intended learning outcomes

On successful completion of this module a student should be able to:

  • Critically evaluate available technological options, and select the most appropriate method for determining the most preferred technology for the specific case study.
  • Demonstrate the ability to work as part of a group to achieve the stated requirements of the module brief.
  • Organise the single-discipline activities in a logical workflow, and to define the interfaces between them, designing an overall multidisciplinary approach for the specific case study.

Applications of Blockchain Technology

Module Leader
  • Dr Chao Long

    This module aims to provide you with data analytical skills to evaluate the advantages of application of Blockchain technology and state-of-the-art of the applications of Blockchain technology in the energy sector. In addition, you will learn essential computer coding skills for writing a private Blockchain network to be potentially used in the energy industry. The scientific and technical concepts of Blockchain technologies and examples of their applications in the energy sector will be taught in this module. The existing challenges in digital energy systems and potential areas of applying Blockchain and its advantages / disadvantages will be discussed in group sessions. A 2-day lab session for simulations will be carried out to allow you to have practical experience and skills for creating a private Blockchain network.

    • Design of an appropriate data analytical toolkit specific to evaluate the examples of applications of Blockchain technology in the energy industry,
    • Blockchain technologies: Analysing the development and scaling/design of the Blockchain technology by evaluating the advantages/disadvantages of the available examples in the assigned areas of applications in energy systems,
    • Blockchain technologies: Programme in an Ethereum platform, lab simulations and the writing of a private Blockchain network to be potentially used in the energy industry,
    • Technology selection: According to the areas of applications and required functions, the most appropriate method for determining the best type of Blockchain (e.g. permissioned or permission-less, proof-of-work, proof-of-stake and proof of authority) will be selected.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically analyse the state-of-the-art of the applications of Blockchain technology in energy industry, and understand the examples of these applications in finance and other sectors as well as in the energy industry,
  • Critically evaluate potential technological options of Blockchain technology, and select the most appropriate method for determining the best type of Blockchain (e.g. permissioned or permission-less, proof-of-work, proof-of-stake and proof of authority) to meet the required functionalities with improved performance for the planning and operation of the energy systems,
  • Design and implement lab simulations individually to create a Blockchain network.

Energy Entrepreneurship


    In this world of downsizing, restructuring and technological change, notions of traditional careers and ways of creating value have all been challenged. People are depending more upon their own initiative to realise success. Never, it seems, have more people been starting their own companies than now, particularly to exploit the World Wide Web. There’s no single Government (in either the developed or the developing world), which is not paying at least lip service to enterprise development. The aim of this module is to provide you with knowledge and skills relevant for starting and managing new ventures across the entrepreneurial life cycle. Moreover, it will prepare you on how to prepare a business pitch to an investor.

    • Entrepreneurial risk, performance and environment,
    • Business planning techniques and their application in entrepreneurial ventures,
    • Venture strategy in dynamic markets,
    • Start-up and resources to exploit a profit opportunity,
    • The evolution of the venture and managing growth,
    • Protecting and securing intellectual capital: IPR and antitrust law,
    • Financial management for new ventures: financing a start-up,
    • The entrepreneurial financing process: buying and selling a venture.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Assess the impact of the business environment on entrepreneurial opportunity identification and exploitation.
  • Critically apply the theoretical underpinning of entrepreneurship to the process of managing risk in new ventures and supporting their development.
  • Compare and contrast how managerial challenges vary across the life cycle of an entrepreneurial venture.
  • Assess the likely financial needs of a new venture and pitch for finance.
  • Develop and write a credible business plan for a new venture.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.