HACKATHON  22: The Future of Household Energy Consumption

HACKATHON 22: The Future of Household Energy Consumption

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The Disruptor

1 Horsten

5612 AX Eindhoven


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Explore how data and AI can change the way individuals understand and consume energy. Join the Energy Transition.

About this event

Join an interdisciplinary group of 5 students and use energy data from private houses to optimize consumption and prevent spoilage of energy. Learn new skills through interactive workshops given by companies and experts. Be entertained by fun activities organized by student teams and socialize with like-minded individuals.

Use Case:

We live in a world where data has become one of the most powerful tools to understand the past, analyse the present and predict the future. Data Analytics is fundamental in decision making, strategy formation, performance optimization, among a range of other aspects. But to what extent is this data valuable if there is no easy access to it or if it is not displayed in a clear, organized and insightful way?

This problem is especially relevant within the Energy sector, where household users have very limited access to specific energy consumption data, preventing them from understanding their own consumption patterns and changing their behaviour. This lack of specific and informative data leads to unconscious usage of electrical appliances, causing an increase in energy bills and consequently in the user’s carbon footprint.

It has been found from research that the monitoring of energy consumption by individual devices and offering real-time data to consumers is highly effective in bringing consumption/behavioural changes as well as better operational strategies for energy stakeholders. This will allow a reduction in unnecessary expenditure in the residential sector, taking a step towards the energy transition.

On the other hand, it is also crucial to understand how such innovative technologies can fit within a sustainable business model. How do you present a technical solution to a non-technical audience? What are the most important aspects to be aware of when pitching your solution? All of this will be answered through an interactive workshop given by an expert in pitching skills.

Group Formation:

Participants must register individually and groups will be formed during the introductory session of the hackathon. If you are a student joining the hackathon alone, that is not an issue. If you dont find your own group, we will make sure to assign you to one.


During the hackathon you can choose one specific challenges to focus on. These can be observed below:

Challenge 1: There is a lack of specific and real-time information regarding individual energy consumption. How can AI and big data be used to show exactly what you consume at what time and provide automated advice?

Develop Models to Predict Individual Appliances’ Power Change

With only the total power, people are unable to find out at what time which appliance consumes the most. This is one of the main reasons why we wish to break down the total power into individual power. But then we will need to come up with an algorithm to do this task for us. Fortunately, energy data consists of time-series data per second. It can also come from multiple households. This results in big data and will be suitable to be analyzed using machine learning and artificial intelligence. Try to gain insight from the model built and evaluate its performance!


Challenge 2: More data collection is required for greater accurcay, enhanced analysis and consequently a more sustainable future. How can this data be generated from the original user data?

Obtain More Training Data to Improve Prediction Performance

In most users' houses, due to the lack of resources, only the overall consumed power is recorded by the smart meter. The public cannot record data per appliance easily, which means most of the data is unlabelled. But with only the limited online datasets, the existing labelled data might be inadequate for accurate prediction of data for different households. Can more training data be obtained by ways like augmentation?


Challenge 3: Every one of us have a different consumption behaviour and pattern. How can a single model be applied across distinct households and achieve the same level of accuracy?

Online Model to Adapt New Data Quickly for Better Prediction Performance

Power usage of appliances among households over time can vary a lot due to the brand and people’s behaviour. If only the historical data is used for training the model, the result cannot be up to date, leading to error in prediction. Since new energy data is obtained every second, can we make use of the new data instantly to adjust the model based on the current household environment and hence improve the prediction performance?


Challenge 4: It is not only about how much information is available, but also how this information is presented and visualised by the end customer.

Interactive Data Visualisation to Present the Users with Useful Information

Nowadays users don't have access to structured and detailed data. This prevents them from understanding their behaviour, etc. Moreover, the general user might not have a very insight into the predicted values. Hence, after energy data per appliance is predicted, we need to convert the raw data to useful information so that the users know what to do easily. What type of interactive visualisation would be a good way to illustrate the information?

Prizes per Team (Besides a weekend of free food, drinks and activities):

- 1st Place: 400 Euros in Vouchers

-2nd Place: 200 Euros in Vouchers

- MyFuture Activity Points

- By registering to the hackathon, participants agree that the challenges and their results are facilitated by and under the rights of SimEnergy-

The Hackathon 22 was made possible in part by a financial contribution from the European Fund for Regional Development, OPZuid and the Province of Noord Brabant in the context of the HTSS project.


June 11 (Saturday):

9:30 – Intro Use-Case (SimEnergy) + Presentation of Challenges

10:00 – Group formation + networking + brainstorming in groups

11:30 – Case Work

13:00 – Lunch

14:00 – Pitching Workshop

14:45 – Case work

16:30 – Activity outside + food/drinks break

17:15 – Case work

19:00 – End day 1

June 12 (Sunday):

9:00 – Coffee + tea + biscuits

9:30 – Final Presentations

12:00 – Lunch

12:45 - Award Ceremony

13:15 - END

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