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Artificial Intelligence for Renewable Energy Renewable Energy Institute

Highlights
Tuition fee
782 EUR / full
782 EUR / full
Unknown
Tuition fee
782 EUR / full
782 EUR / full
Unknown
Duration
3 months
Duration
3 months
Apply date
Anytime
Anytime
Unknown
Apply date
Anytime
Anytime
Unknown
Start date
Unknown
Start date
Unknown
Taught in
English
Taught in
English

About

In this Artificial Intelligence for Renewable Energy course offered by Renewable Energy Institute you will explore the integration of renewable energy and AI, addressing global energy transition challenges.

Overview

Context

This Artificial Intelligence for Renewable Energy course offered by Renewable Energy Institute provides an overview of renewable energy and the energy transition exploring recent progress and the trends driving the global energy transition. This course will also provide a comprehensive introduction to the fundamentals of data, digitalisation and artificial intelligence. These sessions will equip attendees with an understanding of the main methods in the field of AI with a particular focus on neural networks and deep learning. Building on these fundamentals we will explore the application of AI to solve problems in the renewable energy sector from load forecasting to predictive maintenance and network control and optimisation. These sessions will feature case studies that draw on the latest research in these areas. The course concludes with an exploration of the impact of AI and data centres on energy consumption and future directions of AI research.

The curriculum will cover entry level theory before building upon this with more advanced content.

Our courses have been studied by participants from multinational organisations, small & medium enterprises, NGOs, educational establishments and governmental bodies.

Upon completion of this course, participants will receive a certificate of participation and be eligible to take the GMC exam.

The internationally recognised Galileo Master Certificate (GMC) has been achieved by participants worldwide for over 40 years from organisations such as Coca Cola, Mitsubishi, United Nations UNDP, Siemens, Cambridge University, Oxfam GB, Tesco, the Royal Air Force, Ferrero, Honeywell and many more.

Programme Structure

Courses include

  • Energy Transitions and Renewable Energy Technology
  • Trends in Renewable Energy
  • Data and Digitalisation in the Energy System
  • Deep Learning for Electricity Load Forecasting
  • Predictive Maintenance of Renewables using AI
  • Battery Energy Storage Systems and AI
  • Reinforcement Learning and the Learning to Run A Power Network Challenge
  • The Impact of AI on Energy Consumption
  • The Future: Progress on AI for the Energy Transition

Key information

Duration

  • Part-time
    • 3 months

Start dates & application deadlines

More details
  • Online On-Demand: April 
  • Virtual Classroom: 5th - 6th March - 2 days

Language

English

Delivered

Online

Campus Location

  • Edinburgh, United Kingdom

What students do after studying

Join for free or log in to access our complete career info list.

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.

Other requirements

General requirements

  • The Renewable Energy Institute’s professional development courses are open to everyone who has an interest in renewable energy and energy efficiency. The courses will take you through the basic concepts of a subject before advancing to in-depth knowledge, so they are suitable for people of all levels of experience.

Tuition Fees

Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
  • International

    Non-residents
    782 EUR / full
    782 EUR / full
  • Domestic

    Citizens or residents
    782 EUR / full
    782 EUR / full

Additional Details

  • Online On-Demand: €782 (GBP 678)
  • Virtual Classroom: €1,154 (GBP 1.000)

Funding

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Our partners

Artificial Intelligence for Renewable Energy
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