Tunisia has witnessed growing deficits in its energy balance over the past two decades. This trend is largely the result of increasing energy consumption in all economic sectors, coupled with the decline of hydrocarbon production. This led to an energy deficit amounting to 50% in 2019 compared to 7% in 2010, thus leading the country to become more dependent on imported fossil energy. The electricity generation mix is dominated by natural gas, while renewable energy resources represented only 3.0% in 2019. This strong dependence on natural gas has serious implications for Tunisia’s energy security, since domestic production of gas has stagnated to the point of even declining in recent years.
Today, the main source of electricity in Tunisia is oil and gas. In fact, the electricity generation mix is dominated by natural gas at 97.5%.
The strong dependence on natural gas has serious implications for Tunisia’s energy security, as domestic production of natural gas has stagnated and even declined in recent years.
The recent increase in energy imports highlights Tunisia’s economic and social vulnerability amid volatile international energy prices, further amplified by the devaluation of the Tunisian dinar (TND). In fact, the energy import bill is increasing yearly. In 2019, the energy import bill was around 10 000 million TND only for Natural gas and oil products.
In this project, we aim to encourage investments in the field of renewable energies. This is by developing a tool to determine what kind of renewable energy is the most suitable for a given region in Tunisia.
Through this work, we want to:
● Diversify the energy mix and integrate renewable energies
● Strengthen energy efficiency
● Preserve environment
● Reduce the energy import bill
● Provide a guide for investors and researchers
● Create a heatmap showing the best locations for renewable energy investments in Tunisia
The main learning outcomes for the challenge are:
1. Data collection and wrangling: Collect useful data related to the problem.
2. Data exploration and analysis: Clean and analyze data to extract useful patterns and information.
3. Modelling: Try different modeling approaches and use suitable metrics to solve the problem.
4. Deployment: Prepare the solution for deployment and production.
5. Results communication: Present the outputs, outcomes of the projects, and their utilities.