This Year's
Energy communities have emerged as a powerful means to allocate and distribute energy equitably. Recognizing their importance, the European Union recently introduced the Energy Communities Repository, which defines different types of energy communities, focusing on generating social and environmental benefits rather than financial profits.

The intended outcome of this challenge is to develop an optimization algorithm that proposes potential energy communities in the city of Ghent, considering factors such as renewable energy production sites, energy consumption patterns, and the inclusion of residents vulnerable to energy poverty. Additionally, the algorithm should be designed to be scalable, allowing for its application in other cities where similar data has been collected.
Finals Challenge:
Energy communities inclusive of residents vulnerable to energy poverty
by City of Ghent
Winning solution
by AI Wonder Girls
Ocean pollution is a major threat to marine life, with almost 1000 species of marine animals impacted by it, from sea turtles and sharks to small fish and plankton. Additionally, the risk of extinction for marine mammals is high, with around 37% of all marine mammal species at risk.

It is crucial to understand the influence of environmental variables on the biodiversity and abundance of species.

The intended outcome of this challenge is a scalable predictive model that provides insights into the future developments of endangered and invasive species in the Avencas Marine Protected Area. The solutions should consider the developments in species during the observation period and, ideally, be easily scalable to be used in other marine ecosystems with similar data.
Semi-Finals Challenge:
Avencas Marine Protected Area: Predict the Future of the Local Ecosystem and its Species
Cascais Municipality
Winning solution
by The Bayes Bunch
Over the last 40 years, 300,000 individuals have moved from the City of Lisbon to its outskirts in the metropolitan area. As a result, public transport journeys within the city dropped from 46% to 22% in 2017.

With public transport usage decreasing and car usage increasing, it's crucial to analyze mobile device data to determine the main mobility flows and understand commuting patterns.

The intended outcome of this challenge is a better understanding and visualization of how people move between grids during rush hours. Teams are expected to present a model that can predict those movements and identify potential interventions to improve the commuting experience of people in Lisbon and favor sustainable modes of mobility.
Determining The Main Mobility Flows in the City of Lisbon Based on Mobile Device Data
LxDataLab Lisbon
Winning solution
by The Bayes Bunch
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