Demo Day Insights | Accelerating the Clean Energy Transition | World Energy Council

Demo Day Insights | Accelerating the Clean Energy Transition | World Energy Council

By Rosana de Oliveira Gomes

Two Omdena teams with a total of 50 AI experts and data scientists from 25 countries collaborated with the World Energy Council and the Nigerian NGO RA365 in carrying out data-driven analyses and providing AI solutions to address the Global Transition to Clean Energy.

At a recent Omdena Demo Day, team members Amardeep Singh, Julia Wabant, and Simon Mackenzie shared the results and insights gained from these two projects.

 

The Topic: Energy Transition

One of the Sustainable Development Goals adopted by all United Nations Member States in 2015 aims to ensure access to affordable, reliable, sustainable, and modern clean energy for all by 2030.

Transitioning into a society with cleaner energy is crucial for fighting climate change. Different parts of the world are currently facing different stages of the energy transition. This can be noted both on the implementation of solutions in specific regions as well as in the cultural perception of such transition by societies. Both topics are addressed in the following two Omdena use cases.

 

1. Use Case: AI for Renewable Energy in Nigeria

 

Clean Energy 

 

Nigeria is one of the countries in the world facing the most severe energy challenges. Over half of the country’s population — 100 million people — lack access to electricity. Some of the problems faced by Nigerians include precarious electricity systems, unstable electricity supply, and electricity available only in certain locations.

An alternative to these problems is investing in local and renewable power solutions. Renewable Africa RA365 is an NGO with the mission to end energy poverty in Nigeria by leveraging innovative clean energy solutions and focusing on providing solar energy to vulnerable populations. In this project, the Omdena team partnered up with RA365 with the goal of identifying communities where solar panels would add the most value.

The first task in this challenge was to define what these areas should be: groups of about 4000 people living within a radius of about 500 m, and that are located more than 15 km away from a power grid. Regions close to schools, healthcare centers, and water locations were considered to have a higher ranking of priority, as they can benefit even more from renewable energy implementation.

One of the biggest challenges in the project was the lack of data for population density, making it hard to identify where people need assistance. In order to find out how the population is distributed in Nigeria and determine who is without access to electricity, the team compared nighttime satellite imagery from NASA Black Marble VIIRS against the geographic location of the population using the Demographic and Health Surveys (DHS) program, ground surveys from WorldPop, and the GRID3 dataset. Also, for identifying the national grid location and, therefore, find regions where people live in relation to existing power lines, the team applied Machine Learning techniques on satellite images from the HV grid from Development Seed/World Bank.

 

Clean Energy

Combined two satellite data information on average over a large number of nights and seasons.

 

Clean Energy

 

Finally, the team finally worked on finding, among all these towns without electricity supply, which ones would be suitable for the criteria established for the implementation of a local solar energy system. This was done by clustering 4000 people in a 500 m radius using the DbScan clustering technique, leading to the identification of over a thousand high-potential regions.

 

Clean Energy

 

Clusters of towns with populations between 4–15 thousand people which are suitable for potential off-grid solar navigation in the North of Nigeria.

The Omdena’s team deliverable for this project: A prototype interactive map of the whole of Nigeria identifying the regions with a high demand for electricity and a high potential for solar.

The next steps for this project include a detailed survey for the top target areas in order to identify which locations are most suitable both in terms of infrastructure and cost for implementation of solar systems.

A detailed description of this project and its documentation are available in other Omdena publications. See more about the background of this project in this Omdena article.

 

The Impact

The initiative taken by Omdena and Renewable Africa RA365 has the potential of enabling data-driven investments and policy-making that can change the lives of many people in Nigeria and other African countries.

The data and prototype of this project have been shared with the Lagos State Government agency for solar systems, which is now willing to start the process of mass production already in 2020.

“In order to get this job done, it is not all about providing solutions to these people. We want to make sure that the solutions get to the right people at the right places, and Omdena has really helped us to achieve that.”

Joseph Itopa, Machine Learning Engineer at Renewable Africa RA365

 

2. Use Case: Sentiment Analysis on Energy Transition

The transition away from dependency on CO2 to a more sustainable society dominates the news headlines worldwide, exposing conflicting opinions and political measures driving towards a future with cleaner energy sources. Understanding the clean energy transition at a human-level is crucial to the effectiveness of whatever steps are taken in the direction of a carbon-free society.

Commissioned by the World Energy Council, the world’s leading member-based global energy network, Omdena explored applications of AI in understanding how people in different regions of the world perceive the energy transition and their role in it.

Using natural language processing (NLP) techniques, the team created tools to collect, scrape, and analyze text about the clean energy transition found on different social media sources (Twitter, YouTube, Facebook, Reddit, and famous newspapers). This text data was analyzed using varying methods, such as sentiment analysis, topic modeling, and clustering to reveal the challenges, reactions, and attitudes of citizens around the world.

 

Sentiment Analysis Reddit

Topic “Energy transition” for the USA on Reddit.

 

Visualizations of the results allow for comparisons of sentiments across nations and societies. The analysis was first focused on English speaking countries, as this provides a common basis for comparing text. For this, the countries representative of different continents and development backgrounds were: USA (America), UK (Europe), Nigeria (Africa), and India (Asia).

 

Renewable Energy

Data Analysis of Twitter data.

 

The word cloud representation of the results shows that among the 4 countries investigated, only Nigeria has prominent tweets about “electricity supply”. Similarly, “gas prices” are specific to the USA. However, “renewable energy” is present in all 4 countries.

A part of the analysis was also expanded to other countries and languages, gathering and analyzing tweets related to complaints about “renewable energy cost” in more than 20 countries. The results revealed how local conditions and culture can differ significantly from different places. For example, “technology” was the most relevant concern in the complaint tweets in Brazil and France, whereas in Nigeria these tweets were focused solely on “policy”.

 

Energy Transition

Complaints about Energy Transition

 

Other short and detailed discussions about this project can be found in Omdena publications.

 

The Impact

Though broad conclusions cannot be drawn from these isolated collections of data, the results point to models and data sets that are promising for further development. The analysis carried out by the Omdena team allowed for a better understanding of how natural language processing techniques can be used to capture the opinions and concerns of people worldwide about the clean energy transition.

“The Council has been interested in how public sentiment on energy issues might be tracked, or if this were even possible. That is where this project came in — the team at Omdena explored the broad brief and have proven that the conceptual idea is possible.”

Martin Young, Senior Director at the World Energy Council

 

The demo day recording

 

 

Collaborators from this project

We thank our partner organizations, Renewable Africa 365 and the World Energy Council. as well as all Omdena collaborators (listed below) who made the project a success.

 

Omdenda team members, on the Renewable Energy Nigeria project:

  • Anastasis Stamatis, Greece
  • Daniil Khodosko, Canada
  • Peace Bakare, Nigeria
  • John Wu, Australia
  • Siddharth Srivastava, India
  • Simon Mackenzie, UK
  • Hoa Nguyen, Vietnam
  • Takashi Daido, Japan
  • Jessica Alecci, Netherlands/Italy
  • Jack David, UK
  • Shubham Bindal, India
  • Deborah David, France
  • Qi Han, Singapore
  • Stefan Hrouda-Rasmussen, Denmark
  • Varun G P, India
  • Ifeoma Okoh (Ify), Nigeria
  • Suraiya Khan, Canada
  • Ivan Tzompov, Bulgaria
  • Henrique Mendonca, Switzerland
  • Himadri Mishra, India
  • Sai Praveen, India
  • Jaikanth J, India
  • Krithiga Ramadass, India

 

Omdena team members, on the Energy Transition Social Sentiment project:

  • Syed Hassan, UAE
  • Julia Jakubczak, Poland
  • Marek Cichy, Poland
  • Krithiga Ramadass, India
  • Abhishek Deshpande, India
  • Julia Wabant, France
  • Simon Mackenzie, UK
  • Alejandro Bautista Ramos, Mexico
  • Irune Lansorena Sanchez, Spain
  • Vishal Ramesh, India
  • Elizabeth Tishenko, Poland
  • Shashank Agrawal, India
  • Ilias Papadopoulos, Greece
  • Aqueel Jivan, USA
  • Nicholas Musau, Kenya
  • Matteo Bustreo, Italy
  • Mahzad Khoshlessan, USA
  • Yamuna Dulanjani, Sri Lanka
  • Fiona, USA
  • Murindanyi Sudi, Rwanda
  • Raghhuveer Jaikanth, India
  • Abhishek Gupta, USA
  • Aboli Marathe, India
  • Momodou B Jallow, China
  • Jordi Frank, USA
  • Amardeep Singh, Canada
  • Julie Maina, Kenya
 
 
 
 
 

More About Omdena

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

Using AI to Understand Social Sentiments Toward the Clean Energy Transition

Using AI to Understand Social Sentiments Toward the Clean Energy Transition

By Laura Clark Murray

Sentiment Analysis on Energy Transition commissioned by the World Energy Council and carried out by Omdena

The world is in the midst of an energy transition. This massive shift aims to move away from reliance on fuels that are destructive to the climate, the environment, and people’s well-being. The goal established by the UN is to “ensure access to affordable, reliable, sustainable and modern energy for all” by 2030. While governments, energy companies, and activists dominate the headlines, the progress with infrastructure and technology won’t be sufficient. A successful energy transition for the good of all humanity depends on the action of individuals. Together with the World Energy Council, the world’s leading member-based global energy network, Omdena explored the use of AI in understanding how people around the world perceive this energy transition and their role in it.

“If the world is truly to achieve a state of zero-carbon energy within the next three decades, then it is realistic to say that the role of each individual person and citizen in each community on the surface of the planet forms an integral part of this journey.“ — Sue Stevenson, Director of Strategic Partnership and International Development, Barefoot College

How each one of us views the steps in this energy transition likely depends on our personal perspective. For instance, while an increase in home fuel bills might be a mere inconvenience for an affluent family, it might push someone in a marginalized community into poverty. A gasoline tax to subsidize renewable energy efforts will be applauded by some and protested by others, as was the case with the “yellow vest movement” that ignited in France in 2018. Though outlawing the use of fossil-fuel-based generators will be irrelevant for someone with reliable access to electricity, it may cast those living in energy poverty into darkness.

Knowledge of these diverging views is critical to guiding the global shift to clean, affordable, and socially-just energy. Are individuals aware of the risks and benefits of the move to clean power? Do they believe their personal choices and behaviors will have an impact? Who do they feel should pay the costs of the transition? The World Energy Council commissioned Omdena to explore the effectiveness of artificial intelligence to grasp the attitudes of the world’s populace on these topics.

Omdena is a global platform where AI experts and data scientists from diverse backgrounds collaborate to build AI-based solutions to real-world problems. You can learn more here about Omdena’s innovative approach to building AI solutions through global collaboration.

For this eight-week machine learning project, the team built numerous AI models to perform natural language processing. Known as NLP, this approach to AI is concerned with understanding human language. Social media conversations and news articles addressing energy-related topics served as the data for the project. The NLP models were trained to gather and categorize public conversations about energy transition topics. In the words of Amardeep Singh:

What sets this challenge apart from the rest was the sheer scale of data collected, social channels scraped and data analyzed.

For example, one set of models gathered and analyzed tweets in more than 20 countries that were related to complaints about “renewable energy cost”.

 

Energy Transition

 

As seen in the chart, the modeling revealed that technology is the biggest concern in the complaint tweets in Brazil and France. In contrast, relevant tweets in Nigeria were focused solely on policy. Though conclusions cannot be drawn from these isolated collections of data, this exploratory work has led to an understanding of the boundaries of what can be extracted from public online sources. Omdena Collaborator Mahzad Khoshlessan applied various models to filter for relevant tweets to visualize thoughts, concerns, and sentiments of citizens in the USA, UK, Nigeria, and India. Below is an example visualization displaying the most discussed topics in India.

 

Energy Transition

Word Clouds: India

 

“Here at the World Energy Council, we recognize the opportunity and urgent need to humanize energy transition. Only by working at the human-level, embracing a broader community and addressing the social impacts agenda, will it be possible to achieve and sustain the breakthrough performance required for fast, clean, just, and socially inclusive global energy transition.” — The World Energy Council

 

More About Omdena

 

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

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