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Certificate On Campus

Machine Learning for Smart Transition to Net Zero Energy Systems

3 days
Duration
1020 GBP/full
1020 GBP/full
Unknown
Tuition fee
Unknown
Unknown
Apply date
Unknown
Unknown
Start date

About

The transition of energy systems towards decentralisation and decarbonisation creates novel challenges to energy system management, which necessitates a shift to more intelligent control of the whole energy system.  Cranfield University offers the Machine Learning for Smart Transition to Net Zero Energy Systems programme. 

Overview

Key facts

Machine learning is a powerful means of digitalisation that can be used to leverage the huge amount of data in energy systems to efficiently evaluate and coordinate various system components. This short course will provide with essential machine learning methods and tools to tackle the real-world energy system problems in facilitating the transition towards net zero.Machine learning is a powerful means of digitalisation that can be used to leverage the huge amount of data in energy systems to efficiently evaluate and coordinate various system components. This short course will provide with essential machine learning methods and tools to tackle the real-world energy system problems in facilitating the transition towards net zero.

Cranfield University's Machine Learning for Smart Transition to Net Zero Energy Systems programme will teach you more about this topic. 

Programme Structure

Courses include:

  • of challenges and problems of energy systems transition towards net-zero,
  • process of implementing machine learning methods,
  • Concepts and tools of supervised learning and unsupervised learning techniques,
  • Machine learning frameworks in Python,
  • Practical case study session on energy usage pattern recognition (user profile) applying unsupervised learning,
  • Practical case study session on supervised learning detection of electricity usage anomalies.
  • Who should attend

Key information

Duration

  • Full-time
    • 3 days

Start dates & application deadlines

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More details

Enquire for dates

Language

English

Delivered

On Campus

What students do after studying

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Student insurance

Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:

  • Additional medical costs (i.e. dental)
  • Repatriation, if something happens to you or your family
  • Liability
  • Home contents and baggage
  • Accidents
  • Legal aid

We partnered with Aon to provide you with the best affordable student insurance, for a carefree experience away from home.

Get your student insurance now

Starting from €0.53/day, free cancellation any time.

Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at Cranfield University and/or in United Kingdom, please visit Student Insurance Portal.

Other requirements

General requirements

This course is suitable for engineers, IT professionals, consultant and managers working towards net zero and who want to obtain knowledge on applied machine learning techniques.

Tuition Fee

To always see correct tuition fees
  • International

    1020 GBP/full
    Tuition Fee
    Based on the tuition of 1020 GBP for the full programme during 3 days.
  • National

    1020 GBP/full
    Tuition Fee
    Based on the tuition of 1020 GBP for the full programme during 3 days.

Living costs for Cranfield

700 - 1300 GBP /month
Living costs

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.

Funding

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Machine Learning for Smart Transition to Net Zero Energy Systems
Cranfield University
Machine Learning for Smart Transition to Net Zero Energy Systems
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Cranfield University

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