Build an AI-powered Renewable Energy Resources Guide

By Keith McAleer | August 30, 2018

Contact: James Hodson, james@hodson.io

Short Description: SDG07 aims affordable, reliable, sustainable and modern energy.

Detailed Description: China, Europe and the United States accounted for nearly 75% of the global investment in renewable power and fuels. However, when measured per unit of gross domestic product, the Marshall Islands, Rwanda, the Solomon Islands, Guinea-Bissau and many other developing countries are investing as much as or more in renewables than developed and emerging economies. These positive developments need to be scaled up for a global energy transition. The objective of this project is to model suitable places to build sustainable, environmentally friendly power plants, which would replace non-renewable energy production in the world’s biggest polluters. The model should show which renewable energy resource should be utilized in a specific area with consideration to minimal environmental impact in the immediate area and the highest energy efficiency. The project should simulate changes in the environment after using the renewable resources. Projects will include Machine Learning models, data management platforms, and visualization engines to allow communities to interact with the data and assist in decision making. Successful projects will have the opportunity to present their products in front of community leaders, researchers, and policy-makers at the AI for Good Foundation Global Conference in 2019!

Possible Data Sets:

NASA Earth Data: https://search.earthdata.nasa.gov/search

USGS: https://earthexplorer.usgs.gov/

ESA Copernicus Open Data Hub: https://scihub.copernicus.eu/dhus/#/home

NOAA: Here.

ESA Earth Online: https://earth.esa.int/web/guest/eoli

INPE: http://www.dgi.inpe.br/CDSR/

Indian Geo – Platform of ISRO: http://bhuvan.nrsc.gov.in/data/download/index.php

ALOS: http://www.eorc.jaxa.jp/ALOS/en/aw3d30/

The Aerial Photo Ordering System: https://www.ngs.noaa.gov/web/APOS2/APOS.shtml

VITO: http://www.vito-eodata.be/PDF/portal/Application.html#Home

Global Land Cover Facility: http://landcover.org/