Energy Data Hackdays 2021
- Hackathon
24–25 September 2021, 08:30 @ Technopark Aargau, Badenerstrasse 13, 5200 Brugg
Organised by Opendata.ch, Hightech Zentrum Aargau AG, Zukunftsregion Argovia
The third Energy Data Hackdays took place on 24 and 25 September 2021 at the Technopark Aargau in Brugg – on site, with a COVID certificate. 85 participants, from data scientists and energy specialists to students and retired professionals, worked on 14 challenges from energy utilities, research and administration; the number of partners had grown from seven in 2019 to 23. Several projects picked up work from earlier editions. At the centre were bringing together data from Switzerland’s fragmented energy landscape and making it visible – from smart-meter data and wind farms to balancing-energy prices and “green” charging of electric cars.
Photos
Photo: Nadine Zielinski, Hightech Zentrum Aargau, Opendata.ch
Documentation and recordings
- Energy Data Hackdays 2021 – The results! (YouTube)
- SRF 10vor10, 21.10.2021: «Offensiv digital» – Die Aufklärerin (Porträt Nikki Böhler, teils an den Hackdays gedreht)
- #hackdaysreport Energy Data Hackdays 2021 (opendata.ch, 28.10.2021)
- Ergebnisse auf hack.opendata.ch (Event 46)
- Hightech Zentrum Aargau 24.9.2021: Tag 1 – Das Hacking ist gestartet
- Hightech Zentrum Aargau 25.9.2021: Tag 2 – die Resultate
- fricktal.info 23.9.2021: Energy Data Hackdays im Technopark Aargau
- Elektrotechnik ET 4.11.2021: Erfolgreiche Energy Data Hackdays im HTZ
Challenges
- Short-term Photovoltaic Forecasting – EPFL
Challenge description
Generation of short-term (sub-second) probabilistic forecasting of photovoltaic
What is it about?
The goal of the challenge is to generate short-term (sub-second) probabilistic forecasting of photovoltaic (PV) generation and load consumption when only their aggregated prosumption is being monitored. The challenge would involve the disaggregation of the monitored prosumption into PV generation and load consumption in real-time.
Data
The available dataset includes high-resolution historical measurements (up to 100ms) of the nodal prosumption and the global horizontal irradiance (GHI) from a MW-scale distribution grid at Aigle.
@Distributed Electrical Systems Laboratory DESL / EPFL
- Cybersecurity Navigator for Building IoT – Bundesamt für Energie
A tool to informing about IoT cybersecurity and data protection, to support digitalisation in the building sector.
Challenge description
Digitalisation can make a relevant and positive contribution to climate and energy targets in the building sector. To achieve this, the interaction of the individual trades, disciplines and technologies in buildings must be improved. IoT systems are a key technology for achieving the full potential of energy efficiency in buildings. A current hurdle is the low transparency and acceptance of the market towards IoT systems.
We assume that transparency in the area of data protection and cyber security can increase acceptance in the market for IoT systems and that this will have a positive impact on energy efficiency in buildings.
A preliminary project exists in which students of the HSLU analyse and categorise more than 80 guidelines, standards and guidance documents in the area of data protection and cybersecurity.##Challenge:
We create a cyber security tool! But it goes even further: with a public information security tool, we can provide transparency in the area of cybersecurity and data protection to all interested actors in the building market (investors, planners, manufacturers, managers and tenants), and thus support digitalisation in the building sector.
By means of a user-friendly interface, users of the tool could get a list of documents relevant to them based on a selection of criteria.##Data:
Collection of relevant documents in the field of data protection and cybersecurity(1) The preliminary project was developed within the framework of the "Connectivity in buildings" project launched by energie-cluster.ch. The aim of the project is to identify the current challenges of connectivity in buildings (KiG) and to develop possible solutions for optimisation. Demands and expectations regarding the interaction of technical systems with each other, data protection and cyber security as well as the acceptance of users and the interaction between technical systems and users will be discussed jointly with the involvement of all stakeholders.
- Local Sustainability booster – Bundesamt für Energie
Which features boost local sustainability? - Impulses and hurdles for sustainable development on municipality level
Challenge description
##What is it about?
In order to achieve the goals for the swiss energy strategy we must increase sustainable energy production and consumption.
There is already information available for the current status of the expansion of PV, renewable heating and e-mobility. The web tool "Energiereporter" (https://www.energieschweiz.ch/tools/energiereporter/) shows the sustainable development at municipality level.
Visitors can look up on the website the development in their municipality in the areas of electric mobility, solar power and renewable heat.
We want to find out which are the key influencers of these sustainable indicators at municipality level.
It would be a great added value for the municipalities to know which are the most important influencing factors for their sustainable development.##Challenge:
With the complete data set of the Energy Reporter including the sustainability KPI's and other (e.g.socio-demographic, weather, topographic, infrastructure, political and electricity-costs) data on municipality level, the participants could identify the most important influencing factors leading to sustainability. A small machine learning project would be suitable for this analysis. Participants are free to choose the algorithm or statistical method.##Data:
csv with all municipalities and indicators of the energy reporter.
Csv with all municipalities, attributes on population demographics and electricity prices.
In addition, a wide range of municipal data is publicly available. We encourage participants to use opendata.swiss to find interesting data at the municipality level that can explain sustainable development.This project is a follow up of the challenge proposed at the Energy & Climate Hack. Accordingly, the precise frame of the challenge might be influenced through the results of the teams.
- Merge big data from today with historical ones – Energy Science Center ETH Zürich
Impulses and hurdles for sustainable development per municipality
Challenge description
slides
What is it about?
The Swiss energy system is transforming, but how? How did it change from last year to this year, from yesterday to today? How will the forecasted energy system for 2050 differ from today’s one? So far, we lack smart approaches to identify present and future changes in the energy system that go beyond aggregated indicators such as annual electricity demand changes or new power generation capacities installed.
Challenge
In this challenge, we ask the team to develop a platform, in which users can upload big energy data (e.g., time series of electricity generation) and compare their data with other historical or model-based energy data, using novel indicators of energy system changes.
Pitched by https://nexus-e.org/
Presentation
- Smart Meter Open Dataset – NCCR Automation
Build a platform where people can freely donate their energy data to society
Challenge description
What is it about?
Although we all are producing more and more data, access to this wealth of information is becoming increasingly difficult for data protection reasons.
As a consequence, researchers and developers don't have freely accessible, comprehensible, high-quality datasets on individual European households to build their theories, algorithms and applications from.
We want to build a platform where people can freely donate their energy data to society so that science and research can support our energy transition with fair, democratic innovation.
More information will follow soon.
Presentation
- Spatio-temporal model of an on-shore wind farm – OST – Ostschweizer Fachhochschule
Better market predictions before you put energy into the grid
Challenge description
Why is it relevant?
A spatio-temporal model is of great interest within the wind energy community for more accurate wind speed forecasting, which helps to better estimate power production and to develop optimized control strategies.
The challenge
In this challenge we want to develop a spatio-temporal model of an on-shore wind farm near a coast in Brazil.
The goal of the model is to be able to visualize and investigate the local winds and the interaction between turbines, in form of wake effects, within the farm.Data
For this we provide a dataset that contains the 10-minute average wind speeds, the wind directions and the geographic coordinates of 32 wind turbines and 1 met mast tower for a period of 1 year.
See also: https://www.iet.hsr.ch/index.php?id=18317&L=562
Presentation
- Can you beat the model performance benchmark? – Hochschule Luzern, VIA Science
Do you have a new idea for a model using one of the datasets? Can you improve on the existing models?
Challenge description
Can you beat the model performance benchmark?
VIA seeks models to help address the most important clean energy challenges:
24-hour load prediction
Anomaly detection
NILM (non-intrusive load monitoring)
The challenge:
Three smart meter datasets are available:
Low Carbon London dataset with 167 million rows (link)
Reference Energy Disaggregation Dataset with 1.7 million rows (link)
REFIT Electrical Load Measurements with 114 million rows (link)
Do you have a new idea for a model using one of the datasets? Can you improve on the existing models?
Utilities will be able to “upvote” and “downvote” a description of your proposed model to give you feedback before you begin model development. Interested utilities will reach out to prefered analysts through the secure VIA Insights Market™ (logins provided at the event) and be able to directly test your model on their data. Each utility’s data remains private and secure. You will receive a ranking of the performance of your model versus industry benchmarks.
Requirements:
Models must be in Tensorflow 2.0 protocol buffer (.pb) format and a link to your model must be submitted as a comment in your model proposal page on VIA Insights Market™.
Presentation
About VIA:
VIA is powering the clean energy revolution. VIA's AI platform helps organizations drive operational efficiencies and collaborate across the energy ecosystem by overcoming challenges related to data quality, data scarcity, and data privacy. We work with governments and the largest power providers in the US, Europe, and Asia. Now, we need your help to make an even bigger difference.
- Digital twins for domestic heating systems – Hochschule Luzern
How can digital twins help us to safe energy while keeping our appartements warm and guaranteeing that sufficient hot water is available?
Challenge description
Digital twins for domestic hot water and heating distribution systems
What is it about?
In Switzerland, heating and cooling is responsible for 80 % of the residential energy demand. To keep
appartements cosily warm and to provide always abundantly cold/hot water at any time. However,
complex energy and water systems are required to provide this service 24/7. Motivated by the IOT
trend, also the number of sensors installed and recording data from residential energy systems is
steadily increasing.
To operate these systems energy efficiently, clever control strategies are required to prevent
overprovision of hot water or overheating of the rooms. In particular, digital twins attracted recently
much attention because these (data-driven) system models enable the optimisation of the energy
system without touching it. Join this challenge to explore new applications of data from your home
or to actually build digital twins for hot water provision systems or appartements.The challenge:
In this challenge you will have the possibility to explore the multiple benefits of digital
twins. Depending on your background three different directions of the challenge are waiting on you:Hot water provision: Based on measurement data of a domestic hot water system build a
digital twin of the system and explore how well you can predict the hot water demand of the
system?Appartement: Start from monitoring data of an apartment and develop a data-driven digital
twin to predict its behaviour based on external parameters such as the solar radiation.Ecosystem: Which benefits can digital twin provide in the residential context? Which new
applications could be realised? Who owns a digital twin? Which fears could arise?Independent of whether you are an energy expert, a data scientist or an enthusiast, this challenge
offers you the possibility to contribute to the energy turnaround in your home.Data:
Two disparate datasets are ready for this challenge:
Monitoring data from two identical domestic hot water preparation system in different
buildings. This data set is ideally suited if you would like to develop a digital twin yourself or
to showcase the prediction of the hot-water consumption of different users.Monitoring data from a flat including time-resolved room temperature measurements, heat
fluxes to the rooms and solar radiation data.Presentation
- Customer data analysis – AEW Energie AG
Challenge description
Identification of patterns and correlations of anonymised electricity customer data for the development and optimisation of products and services, processes and customer-utility communication.
The Challenge:
The aim is to develop an approach that complies with data protection requirements and allows Swiss utility companies to a) correctly classify their customers, taking into account various data sources and influencing factors, and b) develop products and services tailored to their needs. Furthermore, the application should contribute to improving internal processes.
Data:
anonymised master data
- Trends in tertiary energy prices – Swissgrid
Trends in tertiary energy prices
Challenge description
Tasks
Get yourself familiar with the TRE market in Switzerland
Analysis of historical data (TRE and TERRE bidding prices and volumes, TRE and TERRE activations) to derive patterns of seasonality in the dataAnalysis of the TRE market before and after TERRE introduction in October 2020
Focus on the selected factors (such as intraday prices) and identify the influence on the TRE energy prices
Note: signing a legal agreement with Swissgrid is required to access some of this data.
Presentation
- Pre Qualification Test – Swissgrid
deriving a representive frequency profile, with which TSO can certfy the providers for the FCR service.
Challenge description
##Tasks
Analysis of historical data (frequency deviation measurements for FCR, control signal sent by Swissgrid for aFRR) to derive patterns in the data (e.g., using Machine Learning techniques, such as clustering)
Identify the most prominent patterns in the data and collect them into a set of representative signal extracts
Combine the signal extracts into an overall signal that is representative enough of various scenarios, for example, base-case signal, worst-case signal with regards to power capacity, worst-case signal with regards to energy capacity, worst-case signal with regards to ramping power, etc.
Determine the list of tests to be included in the new prequalification test, making use of the signal extracts among other things
Note: signing a legal agreement with Swissgrid is required to access some of this data.
- Climate-Neutral Cities - St.Gallen Example – St.Galler Stadtwerke
Empower actors to contribute to the climate neutral goals until 2050 in cities.
Mathias MoserAnita NanevaRossella HutaMonika GöldiMartin KoberChallenge description
The Challenge
The participants will develop a solution to push people, companies and public authorities to contribute to the climate-neutral target of the city of St.Gallen.
The team will first identify which are the main levers to reach this target (sectors, stakeholders) thanks to a provided Dataset.
Then, the team will look for an adapted way to inform the relevant stakeholders of the leverage they have and help them identify wish actions they should first take to contribute to the climate-neutral target of the city.
Participants can bring:
knowledge or interest in one or several of these areas:
Open Data sources, Footprint Tools, Data Analysis, UX/UI Design, Nudging, Customer Centricity, Gamification ;
domain knowledge or interest in one or several of these topics:
Energy, Transport, Food.Why
The origins of the energy strategy of the city St.Gallen date back to 1992. Driven by the urge to make St.Gallen a green and energy efficient municipality, the strategy is based on a clear vision combined with a wide range of measures on various policy and implementation levels. The acquisition and consolidation of data has been the backbone of St.Gallens ap-proach for many years.
In 2020 the city council has decided to further enhance the energy strategy to reach climate neutrality in 2050. Whereas activities until now primarily focused on energy-related measures, the energy strategy is currently extended to include the wide field of consumption. New approaches to foster participation, promote the need to act, define effective measures and monitor achievements are in need. Data will play a significant role.
Data Sources
Several datasets are available from the city, from research projects or from open data sources:
Material Flow Switzerland (MatCH) Database - EMPA
Gebäudedaten St.Gallen - Energy production and consumption
District heating areas of the city of St.Gallen
The Data are available on the Open Data Platform of the city of St.Gallen with login restrictions. A personal access with login will be given to the participants.
Previous work - Energy Data Hackdays 2020
Clickable prototype
Miro Board
https://miro.com/app/board/o9J_luhs87w=/
Live Platform
https://sgsw-json-test.bubbleapps.io/version-test/rechner
Presentation
- Where does my electricity come from? – geoPro Suisse AG
The path of electricity from the end consumer's point of view.
Challenge description
The Challenge
By clicking on their own house and selecting their own electricity mix, consumers should be able to understand topographically where their electricity comes from. Which is the nearest distribution box? Where do other lines branch off? At which transformer station does "my" electricity pass? And which substation is responsible for my supply?
It should be possible to trace back topographically correctly to the boundary of the electricity supplier. Standard network states are used for this. At the physical boundary of the plant, the selected electricity mix is represented schematically and visually: Hydropower (river power plant, storage power plant), nuclear power plant, coal power, solar, wind, ...
- Trend scouting for the energy industry – Kompetenzzentrum Energy Management (ior/cf-HSG)
Identifying trends in the energy industry with the help of modern AI- and statistics-based information and text analysis methods
Challenge description
What is it about?
In 2018, the University of St.Gallen, in collaboration with Swisspower Innovation and other practical partners, identified the megatrends relevant to the energy industry on the basis of various workshops.
The Challenge:
As part of the Challenge, the trends already found will be verified or new trends identified with the help of modern AI- and statistics-based information and text analysis methods.
Data:
For this purpose, programs and presentations of selected conferences from the energy sector of the past 5 years will be made available.
Other relevant, publicly available data sources could be job boards (jobs.ch), startup monitors (Swiss Environment and Energy Innovation Monitor) and annual reports of energy utilities. - Electricity “Heartbeat” 2035 in the Canton of Zurich – EKZ
What do changes in the way and in the intensity electricity is consumed mean for our grid?
Challenge description
What is it about?
Climate change forces us to change the way we are consuming and producing energy. The “Energiestrategie 2050” of the Federal Council aims at reducing the environmental impact of energy, foreseeing an increased decentralized energy production in the coming decades. At the same time, technological developments, i.e. electric cars and heat pumps, change the way and intensity electricity is consumed. But what does all this mean for our grid? What is the future of electricity consumption and production? How does the picture change geographically and with time? Can we better understand these changes through visualization? We think we can!
## Challenge:
We want to create geospatial visualizations in order to understand where and when electricity is produced and consumed in the grid region of EKZ. We will provide the data and we are asking for your creativity and ideas in order to create meaningful visualizations that shed a bit more light into the future! Here is a little motivation from similar visualizations for traffic intensity in Manhattan, New York:Data:
We will provide 100% synthetic – but realistic – consumption und production data for 2035 in transformer granularity for most of the transformers in our grid region. The data were generated in the scope of a Master Thesis in cooperation with the Power Systems Laboratory of ETH Zurich. The data is generated based on different assumptions and scenarios of the electricity production and consumption for the Canton of Zurich in 2035
Presentation
- “Green” charging E-Mobility – EKZ
When is it “greener” to charge an electric car?
Challenge description
## What is it about?
Electric mobility is a “green” alternative to conventional gasoline and diesel cars. But can it get any “greener”? Can we find a way to reduce the environmental impact of electric car charging even more? When is it “greener” to charge an electric car in winter or in summer? Let’s find out!Challenge:
With the help of data of CO2 equivalent emissions of electricity production and consumption in Switzerland, we want to analyze what is the environmental impact of charging electric cars. In addition to this, we want to develop a logic in order to charge electric cars as “green” as possible during the day, taking into account the environmental impact of electricity with respect to the time of the day and the different seasons.
Data:
We will provide data of CO2 equivalent emissions of electricity production and consumption for Switzerland and charging stations consumption data. Open data from various sources can also be used.
## Presentation
- Interoperability plug-test for smart meter’s local CII – Gantrisch Energie AG
Empower citizens to use their own energy data. Using the smartmeter's local interface for visualisation and automation
Challenge description
Introduction
Smart Meters currently being installed in Switzerland serve mainly to liberalize the electricity market, but they hardly deliver the promised energy savings because they do not yet provide customers with easy access to their data.
StromVV §8a defines the characteristics of smart metering systems requiring a local customer interface (CII), which should allow customers to easily access their own consumption data. Unfortunately, these interfaces are not implemented uniformly by the manufacturers and are rolled out by distribution system operators (DSO) in different configurations. This makes it very difficult for the normal customer to get access to his data.
Motivation
The easy and free access of consumption data is a crucial step in the energy transition and shall help to provide the following contributiongive customers transparency about their consumption in real time
change customer behavior in a sustainable way to save energy
create innovations that help customers to optimize their own consumption with self-generation (PV) and controllable consumers (car charging station, heat pump)
Idea
Reading of different types of smart meter CII (DLMS, IDIS CII, DSMR-P1, MBUS), used by different DSO (EKZ, AEW, EWB, Romande Energie) by means of a metering data adapter which provides the data to the customer via a harmonized MQTT profile that can be used via an open visualization and automation. The customer can thus view the data at any time and also use it to align his own automation to optimized self-consumption. The MQTT topics should be standardized to provide interoperability between different smart meters and various applications.
Data
The data from the different meters are sent via MBUS or DSMR to a adapter which converts the data into MQTT.
Different meters are provided by the DSO.
The MQTT stream is then integrated into OpenHab 3.0 and ioBroker.
TasksSetup of the metering systems
Conversion of MBUS-DLMS/COSEM data into MQTT using the open-source library from gurux.fi
Adaption of the DSMR-P1 Protocol to MQTT
Documentation of the interoperable MQTT interface
Integration of the smart meter data into ioBroker and OpenHab
Previous work at Energy Hack Days 2020:
Read your own Smart Meter
Unleashing the Swiss Smartmeter's CII
DSMR-P1 Adapter "gPlug" from forumE.ch
Presentation
provided by Peter Kreutzer & Hermann Hüni, GEAG
- ReFi - Regenerative Finance for smart energy communities
Tokenisation of Energy and Fractional Ownership of Digital Assets provide new possibilities for financing PV-projects.
Challenge description
Background
New information and communication technologies (ICT) are creating whole new industries and opportunities. This will have structural implications also for energy utilities, DSOs, energy communities (EC) and prosumers. The decentralized finance industry is using new technologies and token economics (TE) that create novel financial applications and business models that can have a significant impact on how the energy transition can be financed. The concept of fractional ownership has been identified as a new possibility to create innovative business models [1]. Utilising tokenisation and DeFi concepts, we could be able to develop flexible, scalable and replicable financing instruments.
The Challenge
Implement the concept of fractional ownership, tokenisation of energy and utilisation of open-source blockchain and DeFi protocols. Build a MVP for fractionalising digital asset (ownership), lending, borrowing and auctioning of those ownership fractions that allows PV systems owners to (re)finance existing or new projects, and trading ownership rights of their projects.
Skills
You do not have to be a full stack developer. Basic knowledge of solidity, javascript or how to build DApp using the react framework next.js and IPFS would be very helpful. Also, if you are new in blockchain you are very welcome to join the project. During the hackathon I will provide information about the architecture, existing code solutions and tools.
Vasileios Panagiotidis|[e-swissolar AG] (http://www.e-swissolar.ch/index.php/en/) , Poststrasse 9, 6300 Zug
References:[1] General Business Model Patterns for Local Energy Management Concepts
Results
Analysis of the off-chain fractional ownership (PV-panel) and crowdfunding process of Solarify
Analysis for a possible on-chain implementation
Implementation of the solution using ReFi's smart-contracts using Remix-IDE
Discussion about the solutions - value creation & open issues
Next steps
- Open Habitation – EcoHabitas
Aggregating data to search the sustainability of offered real estate
Jonas FürstenbergerMatthias MeierCarlos AraújoChallenge description
slides
We created a minimal API that aggregates data from GeoAdmin (Swisstopo) and PVGIS (EU) to calculate a score for any building property which is in the Open Data sources. Plans to build on this and add crowdsourcing capabilites have been discussed.
Please note: At the moment, the calculation of PV production does not work properly. But the rough procedure as well as the API-queries are correct. We have to change little things and we will update as soon as possible.
Presentation
Next steps
(1) Complete basic functionality of the prototype
https://marshmallow-sqlalchemy.readthedocs.io/en/latest/index.html to persist the DB properly
https://swagger.io/docs/specification/describing-request-body/ to create a valid spec
https://frictionlessdata.io/standards/ for making the data portable and valid
https://github.com/PWZER/swagger-ui-py/issues/29 to integrate OpenAPI
(2) Build on the (Falcon based) Web API
https://github.com/falconry/falcon/wiki/Complementary-Packages for extra features
https://github.com/arivero/SampleAnonAPI is an example Falcon data anonymization project
https://github.com/vicinityh2020/vicinity-vas-dreven example Falcon smart charging project
https://github.com/bookla-foundation/meerkat more info on RESTful Falcon apps
https://apispec.readthedocs.io/en/latest/using_plugins.html for integrating Marshmallow
(3) Make further improvements to the data model
https://www.opendataphilly.org/dataset/housing-production
https://data.stadt-zuerich.ch/dataset/bau_best_geb_jahr_gebart_bauperiode_quartier_od5087
https://www.housing-stat.ch/de/home.html
http://carboncoop.greenopenhomes.net/
https://wikirateproject.org/About_Us
Challenge
How sustainable is this property?
The building sector still has huge impact on climate change, altough co2-reduction is relatively easy (and cheap) to reach.
When choosing the next property (rental or buying), the environment plays a role that is too small. As a result, investors are less interested in green solutions. One reason why ecological aspects are not taken into account in the selection is that no (or not enough) information is available for home-seekers.
let's provide this information!
"collect and combine" public information of offered real estate (heating system and - if time remains - photovoltaics, building standard, access to green mobility ...). Provide this information for real estate marketplaces.
Programme
Friday, 24 September
- 08:30–09:15
Registration with COVID certificate, coffee & croissants
- 09:15
- 09:45–10:45
- 10:45
Team building and start hacking
- 12:30
Lunch
- 17:30
Project documentation
- 18:00–18:30
- 18:30
Dinner, then hacking open end
Saturday, 25 September
- 08:00
Coffee & croissants, continue hacking
- 12:30
Lunch
- 15:00
Deadline for slides and documentation
- 16:00
- 16:15
- 17:15
- 17:30
Apéro
Read the original announcement
We are happy to announce that after the good experiences and results of the 2019 and 2020 Hackdays, the third edition of the Energy Data Hackdays will take place on
Friday the 24th and Saturday the 25th of September 2021 in Brugg.
We are pleased that all partners of the 2020 Hackdays have agreed to participate again and that we have already been able to attract new partners. This promises exciting and concrete challenges.
So SAVE THE DATE, SPREAD THE WORD and JOIN US!
We have a brand new dedicated homepage ! Go and have a look
You can already register **here**
Provisory program:
A COVID certificate is required to attend the event and will be requested at the entrance.
Friday, 24.09.2021
09.00 Welcome Coffee & Croissant
09.15 Introduction09.30 Challenge pitching
10.30 Group building
11.30 Start hacking
12.30 Lunch
13.30 Input Talk (to be announced)
14.00 Continue hacking
19.00 Delicious Dinner
20.00 Continue hacking – open endSaturday, 25.09.2021
08.00 Individual start hacking with breakfast
12.00 Lunch
16.00 Presentation of your prototypes
17.30 Apéro and celebration
Organization & Sponsorship
The Energy Data Hackdays is an initiative of the Zukunftsregion Argovia, the *Hightech Zentrum Aargau* and the Swiss association Opendata.ch.
The Hackdays are made possible by:
If you would also like to take part and support the event as an energy data supplier, as a financial or communication partner, please contact us at info@opendata.ch!
Organising team
Nikki BöhlerGeschäftsführerin, Opendata.ch
Maud ChâteletProjektleitung und Moderation, Opendata.ch
Oleg LavrovskyDaten und Infrastruktur, Opendata.ch
André GolliezVorstand, Opendata.ch
Peter MorfProjektleitung, Hightech Zentrum Aargau
Melanie GygliHightech Zentrum Aargau
Nadine ZielinskiFotos, Hightech Zentrum Aargau
Matthias EifertProjektleitung, Zukunftsregion Argovia
Host
Sponsor
- Nussbaum RN
