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Managing energy data: Advanced analytics
Managing energy data: Advanced analytics
Develop new business models and added-value services based on a data science strategy
Managing energy data: Advanced analytics | April
Managing energy data: Advanced analytics | June
Managing energy data: Advanced analytics | September
About this course
Smart meters are vastly spreading worldwide. Together with the IoT revolution, we have an increasing access to massive sets of data - the energy big data. This paradigm is completely shifting current energy systems as more intelligent ways of managing energy have the potential to be developed. However, companies in the energy sector are still far from digitalizing their business model and their services, falling behind other sectors such as banking. Hence, a significant opportunity exists on extracting value from the energy big data and the way to do that is by implementing data science.
This course is designed for any professional in the energy sector that wants to understand the impact of data science in creating added-value services from raw energy-related data. The goal of this course is not to explain how to do data science but rather how to develop, implement and control data science projects in energy businesses. Thus, a high focus will be given to the definition of real-life problems that businesses have been experiencing and how to tackle them with data science.
Is this course for you?
This course is targeted at (tech or non-tech) senior operations personnel in energy utilities (DSO, energy suppliers) and industry, graduate students or entrepreneurs in the energy sector.
Basically, any professional in the energy sector that wants to improve the value of her or his company by taking advantage of energy-related data.
How does the course boost your career?
With this course, you will be able to:
- Explain how data science can bring value and disrupt the energy sector with big data such as data from IoT, end consumers and smart meters.
- Evaluate the implications, challenges and benefits of implementing data science projects in an energy company.
- Apply lessons learned from real-life examples of data science applications to energy big data to their own business.
- Supervise the development and implementation of a data science project, ultimately creating a new value that was not explored before with available data in their energy business.
Teaching methods and materials
This course is designed to provide you an interactive architecture to help you grasp the concepts in a direct way with:
- Videos from experts with high credentials in the field.
- Self-evaluation simple exercises such as multiple-choices, associations or ‘fill in the blanks’.
- Simple exercises with calculation tools such as Microsoft Excel® that will help you visualize the impact of the various data science challenges in generating value for an energy business.
- Real-life experiences of InnoEnergy startups will also be shared and participants will have interactive exercises to understand how these companies are tackling their data-related challenges.
- Relevant articles that will consolidate your acquired knowledge, helping you sustaining your decisions after the course.
- A tutor, expert in the field with high credentials, available to support you in any question during the course.
Co-founder and COO of Watt-IS, holds a MSc. in Environmental Engineering and a PhD in Sustainable Energy Systems from MIT Portugal, EIT InnoEnergy and Fulbright programs. Has a wide experience in management and research in consumption behavior and data science focused on modeling and optimization with several articles published.
Co-founder and CEO of Watt-IS, Miguel has a wide degree of experience on the energy sector both on the business development and data analytics areas. He has an ICT degree, a MSc. on Technology management and an Executive MBA.
Carlos Silva is a co-founder of WATT-IS since 2012. He holds a degree, a MSc and a PhD in Mechanical Engineering from IST. He also is the author of 30 papers in peer-reviewed international journals in systems modeling and operations research and more than 80 communications in international conferences.