IBM, UN Launch Free-to-Use Advanced AI Energy Forecasting Tools

They harness IBM’s watsonx, Cloud and Environmental Intelligence to let users analyze complex energy issues using advanced AI technology

Heidi Vella, Contributing Writer

September 18, 2024

2 Min Read
The sun shines above a natural gas-fired electric power generating unit from the 1950s at AES Alamitos in Long Beach, California.
PATRICK T. FALLON/AFP via Getty Images

IBM and the United Nations Development Programme (UNDP) have launched two new free-to-access AI-powered interactive models to forecast energy access and model energy equity globally. 

The first is the Electricity Access Forecasting modelwhichuses AI to provide future forecasts of electricity access through 2030 by modeling factors such as population, urbanization, infrastructure and satellite data. It has information from 102 countries, including the Global South and Latin America. 

The second is the Clean Energy Equity Index which the organizations said is the first of its kind. The index uses an AI-powered statistical geospatial model that reveals opportunities and urgency for clean energy development by generating a “score” (0-1) based on environmental, social and economic factors. It has data for 53 African countries.

Users of this platform, which was developed by IBM, UNDP and Stony Brook University, can customize the model to simulate scenariosand evaluate which factors have the greatest impact. 

To develop both applications IBM and the UNDP harnessed technologies such as IBM watsonx, IBM Cloud and IBM Environmental Intelligence and collaborated through the IBM Sustainability Accelerator program over two years.

The new models will be available to the general public for free through UNDP’s GeoHub dashboards. GeoHub is a centralized ecosystem of geospatial data and services that provides a platform to easily upload, visualize and analyze datasets, combining time-oriented and geographic data with satellite imagery.

Related:Jeff Bezos Launches $100M AI Challenge for Climate, Nature Initiatives

The organizations said the platforms will enable users — from policymakers to the general public — to analyze complex energy issues through advanced AI technology helping to support data-driven decision making for a just energy transition.

Historically, advanced models such as these have not automatically been made public. But both organizations said they wanted to co-create solutions that would strengthen free access to complex clean energy information and advanced technology.

Justina Nixon-Saintil, IBM’s vice president and chief impact officer said the hubs demonstrate the power of technology to make a lasting, positive impact on the environment and in communities.

“By making innovative models freely accessible to the public, we aim to empower leaders, organizations and community members alike with the insights to make impactful energy decisions around the world,” she said. 

Head of the UNDP SDG Integration Team, UNDP Bureau for Policy and Programme Support Laurel Patterson said the development of data and technology innovation could improve lives and protect the planet.

Related:Consortium to Establish Global Water Monitoring Solution

“The solutions we’ve co-created provide a credible evidence base to help countries make meaningful and practical progress towards a just energy transition. Net-zero investment and people-centered development strategies are fundamental to accelerate the SDGs,” she said. 

UNDP works in about 170 countries and territories, helping to eradicate poverty, reduce inequalities and exclusion, and build resilience so countries can sustain progress.

This article first appeared in IoT World Today's sister publication AI Business.

About the Author

Heidi Vella

Contributing Writer

Heidi is an experienced freelance journalist and copywriter with over 12 years of experience covering industry, technology and everything in between.

Her specialisms are climate change, decarbonisation and energy transition and she also regularly covers everything from AI and antibiotic resistance to digital transformation. 

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