Giving Visibility to Renewable Energy

The ultimate project goal of EnergizAIR Infrastructure was to raise individual awareness of the contribution of renewable energy sources, and ultimately change behaviors. Now ten years later, with orders of magnitude more data, AI/machine learning, cloud, and smartphones in the hands of individuals, this is an idea whose time has come.

Author: Didrik Pinte, M.S., CTO

Renewables’ contributions as energy sources and their part in driving business strategies are rising steadily. Multiyear objectives are appearing across companies and countries that rely on renewables, with terms like climate neutrality becoming more familiar. Environment, social, and governance (ESG) strategies are now used by all major companies, with renewables playing an important role, particularly for major oil and gas operators.

Concurrently, the public is becoming more aware of the drive to utilize renewables. However, positive information demonstrating how renewable energy can fit into their everyday lives is limited. Such visibility at the individual level will be necessary to influence behavior and to gain people’s confidence and support.

In 2010, Enthought participated in EnergizAIR, an ahead-of-its-time project by Intelligent Energy Europe and funded primarily by the European Union. EnergizAIR was designed to provide consistent visibility of the contribution of renewable energy sources by integrating them into standard media weather reports with three objectives: to make European citizens aware of the contribution of renewable energy sources, to help them understand the energy sphere, and to encourage them to support sustainable energy management.

Enthought’s role was to create the data management framework that would retrieve raw energy and meteorological data, store and process it, and provide interpretations, as well as create and distribute reports in various formats to media channels.

The project was successful in integrating renewable energy data with traditional weather data for a media audience of 2.5 million through 19 channels. Enthought worked closely with the EnergizAIR team using rapid iterations and ensuring complete transparency for the test-driven development. This enabled obtaining and implementing quick feedback. The result was a well-tested solution that was flexible and extensible while remaining readable and requiring minimal Python knowledge to extend and modify.

There are a number of possibilities for a similar project today to influence individual behaviors for energy sources and consumption, integrating weather information, with orders of magnitude more data, cloud, AI/machine learning and personal mobile devices. Enthought has similarly advanced over the last 10 years in ways that could meet the challenge. Scalable cloud-enabled infrastructures would be key, integrating the latest AI/machine learning models for data processing, analysis and sharing. Perhaps time for us to approach the EU – we have it covered.

To learn more, read the case study here.

About the Author

Didrik Pinte, CTO at Enthought holds an M.S. in bio-engineering and an M.S. in management from the Catholic University of Louvain (UCL) in Belgium. He is an expert in artificial intelligence, data management, and software development. He served as a research assistant at UCL, developing Python-based integrated water resource management applications.

Share this article:

Related Content

True DX in the Pharma R&D Lab Defined by Enthought

Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders…

Read More

Life Sciences Labs Optimize with New Digital Technologies and Upskilling

Labs are resetting the trajectory for drug development: reducing timelines from years to months; decreasing costs from billions to millions; and gaining an advantage by…

Read More

Configuring a Neural Network Output Layer

Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just…

Read More

No Zero Padding with strftime()

One of the best features of Python is that it is platform independent. You can write code on Linux, Windows, and MacOS and it works…

Read More

Got Data?

Introduction So, you have data and want to get started with machine learning. You’ve heard that machine learning will help you make sense of that…

Read More

Sorting Out .sort() and sorted()

Sorting Out .sort() and sorted() Sometimes sorting a Python list can make it mysteriously disappear.  This happens even to experienced Python programmers who use .sort()…

Read More

A Beginner’s Guide to Deep Learning

Deep learning. By this point, we’ve all heard of it. It’s the magic silver bullet that can fix any complex problem. It’s the special ingredient…

Read More

Webinar Q&A: Accelerating Product Reformulation with Machine Learning

In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products…

Read More

Scientists Who Code

Digital skills personas for success in digital transformation The digital skills mix varies widely across companies, from those just starting to invest in digital transformation…

Read More

Digital Transformation in Practice

Taken from the webinar, the frequent strategy of digitalization-layering digital tools and technology onto existing processes-provides incremental value, which soon flattens out. At the other…

Read More

Join Our Mailing List!

Sign up below to receive email updates including the latest news, insights, and case studies from our team.