ThermoCycle

Presentation:

ThermoCycle_Pitch_Deck_v3.pptxv

Framing the Problem:

What is the problem you are trying to solve? Who does it affect?

Lots of energy is used to cool down AI units, using lots of water which is not sustainable. due to increasing energy prices, less people are able to afford heating especially in the colder months. This especially affects lower income family as well as the elderly who may be more susceptible to the cold. Water is being used to cool down AI units and instead of using this water, our plan is to divert the heat energy to homes, minimising the need to use up the water.

Idea Explanation

What is your idea? How does it fix the problem?

Our solution turns one of AI's biggest challenges into an opportunity as an energy company. AI data centres produce large amounts of heat, but most of it is wasted. Instead, we capture this excess heat and uses it to provide heating for nearby homes.

Reusing this heat can reduce household heating bills by an estimated 30–65% compared with traditional heating systems. This helps homeowners and businesses save money ,while making better use of energy that would otherwise be lost.

Although installing the infrastructure requires a significant upfront investment, the long-term benefits make it a practical and sustainable solution. As the use of AI continues to increase, data centres will generate even more waste heat. At the same time, many existing cooling systems use large amounts of water, putting extra pressure on already limited water supplies.

Our approach improves energy efficiency while reducing the need for water-intensive cooling. Rather than treating excess heat as waste, we turn it into a useful resource that benefits both local communities and the environment. This creates a solution that is cost-effective, scalable, and well suited to supporting the continued growth of AI in a more sustainable way. This is also good as it generates jobs for people as the infrastructure has to be built.

Implementation

Our system consists of three main parts: the frontend, the backend, and the database. The frontend is the website or app that users interact with the website or app that users interact with to view the energy plan, monitor heat savings, and access information about our service. we used ChatGPT through an API key and Lovable, also connected though an API key, to help develop and improve the user interface. The backend works behind the scenes to process data, calculate heat savings, and manage the system. The database securely stores information such as heat usage, energy savings, and user data and community impact data. Together, these components allow our system to efficiently track recovered heat, measure sustainability benefits, and support our social enterprise model.

Challenges

What did you struggle with? How did you overcome it?

We originally struggled with the logistics and coming up with an idea. We overcame this by researching the actual problem further and then working backwards to find a solution. at first we didn't have a target audience which not only made it difficult to come up with ideas but also caused us to run into more issues in terms of profits and affordability. Although there is the challenge of AI activity decreasing at times, producing less heat, this thermal energy could be stored and therefore mitigate this problem.

Accomplishments

What did you learn? What did you accomplish?

We managed to work cohesively as a group and eventually managed to delegate tasks appropriately, making it easier to finish on time.

We received and acted on different criticism utilising several tools to make our idea come to life. We also learnt how to concisely present and our project model to different people.

Next Steps

What are the next steps for your project? How can you improve it?

We could also incorporate renewable energy sources, as AI systems currently rely heavily on fossil fuels, which are finite resources that contribute to climate change and global warming. Additionally, improving the energy efficiency of AI systems by reducing wasted heat would help conserve energy and lower their overall environmental impact. Profits could be reinvested back into the energy company and any additional money could be put back into the local community. Partnering with other AI companies or housing companies could help to reduce the risk of the need for AI data centres being close to homes to minimise the energy lost to the surroundings.

Teamwork

Sana did the presentation and delegated the tasks on the task manager, Ateeya generated the photos for the slides and also helped with the presentation. Esohe recorded the demo video of the website and generated the website. Maliha used chatgpt and claude to generate a prompt for loveable and completed the submission document with Esohe. We all contributed to the ideas as well as the research involved in the task and presented the project. We all worked well as a team and after day 1 we all made sure to communicate effectively on Whatsapp to keep track of how far we had gotten and ensure that we could finish on time.