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Active projects and challenges as of 31.03.2025 06:47.

07 - Governance of DLT 4 Financing Blockchain

Blockchain networks have to be properly desinged and managed


~ PITCH ~

Challenge:

https://hack.opendata.ch/project/685

Assumptions:

ReFi is a permissioned blockchain network that provides a framework for collaboration and facilitates a platform for investing in renewable energy digital assets.

The Idea:

For investing/lending, crowd-investors could provide liquidity to a pool (smart-contract). Fund-seeker could fractionalise (tokenise) ownership of digital-assets (pv-system).

drawing
Click on picture to enlarge

What we did:

  • Stakeholder Analysis

A stakeholder analysis refers to the range of techniques or tools to identify and understand the needs, expectations and motivations of stakeholders. [1]

* Value-Flow Analysis

Value Flows describe flows of resources of all kinds within distributed economic ecosystems. On a blockchain network value flows are created when processes are linked together through flows of resources. [2]

* Implement the 8 rules of Elinor Ostrom

Based on her extensive work, Ostrom offers 8 principles for how commons can be governed sustainably and equitably in a community. [3]

Results:

Value Flows Diagram

drawing
Click on picture to enlarge

Ecosystem Governance in the context of a blockchain network

drawing
Click on picture to enlarge

The creation of non-zero-sum games is not an easy task. Would you like to know more? Play the game [Link](https://ncase.me/trust/)

**Watch video of our presentation:**

Resources / Data:

*[1] [What is a stakeholder Analysis](https://en.wikipedia.org/wiki/Stakeholder_analysis)*

*[2] [Flows of Value - The concept](https://www.valueflo.ws/introduction/concepts/)*

*[3] [Elinor Ostrom's 8 Principles for Managing A Commmons](https://www.onthecommons.org/magazine/elinor-ostroms-8-principles-managing-commmons)*

Next Steps:

Cool urban spaces

Application to formulate and realize suggestions to improve climate and living conditions in your neighborhood - Make your city cool again!


~ PITCH ~

This project is based on challenge 09 (initial idea): https://hack.opendata.ch/project/759

Our goal is to realize a web application where citizens can express and rate suggestions on how to improve urban open spaces (de: urbane Freiräume) throughout Switzerland in terms of the city climate and general living conditions - may the spaces be public or private. The initial idea was focused on suggestions concerning urban heat including infrastructure elements. However, the concept of suggestions to improve the neigborhood can easily and intuitivly be expanded to include suggestions about social and ecological topics in general.

The application will be map and postal address based, where users can add suggestions, such as shade structures (tents), bright colored benches, trees, water points and green facades, represented by different icons, to a specific location and be able to see and participate in active suggestions in their neighborhood. A mockup has been created and can be seen in the header slides.

The application has the goal to facilitate active discussion between the users around the presented ideas and to enable direct action to be taken. It's is similar to "Züri wie neu" which in turn is based on FixMyStreet, mySociety's map-based reporting platform for citizens. But while "Züri wie neu" is about common street problems this new application prototype focusses on proposals in/for urban spaces including private spaces like playgrounds in settlements.

For that purpose, users can rate suggestions and comment critically or constructively. Users can decide to support a certain suggestions, which means they will actively collaborate and work for that suggestion to be further developed and implemented. A chat function between supporters could facilitate the organization and would be the first point of contact for new supporters.

One crucial part of implementing a suggestion is to convince the owners of the respective land - private or public - or at least reach them out or even get their permission. However ownership is often opaque, which is why we strive to integrate features that allow the users to identify the owners of the respective piece of land and contact them.

This is a non-trivial problem due to the lack of available data and services which seems to be related to lay - but that's not the only reason: There seems to exist a lack of digitalization and user-orientation in survey cadastre and land register!

Our approach at the moment is to work with the API of the Swiss Public-Law Restrictions cadastre (de: ÖREB-Kataster) which at least contains contact addresses and phone. However this information is only available about the responsible land register offices which mostly only give ownership information after personal request through phone or e-mail at most. This contact information could be provided or a contact form could potentially be implemented.

This application must be privacy preserving and should be implemented based on open source software and open data, in particular OpenStreetMap data as well as Open Governement Data e.g. from the City of Zurich and Swisstopo.

For more info about the challenge visit 👉 https://etherpad.wikimedia.org/p/CoolUrbanSpaces For more info about data and services on OpenStreetMap (OSM) see 👉 https://etherpad.wikimedia.org/p/OSM (especially useful also for challenge 2, 5, 6 and 8).

Source code back end: https://github.com/lbuchli/cool_urban_spaces_backend

Watch video of our presentation:

~ README ~

Cool City

This project has been created for the Opendata Hackaton 2021 by Felix Reiniger, Lucas Buchli, Fabio Zahner, Remo Steiner, Jurek Müller under the lead of Stefan Keller.

Project Description by Stefan F. Keller

The goal of the mobile/web application is to formulate and rate citizen suggestions on how to improve public and (semi-)private urban open spaces throughout Switzerland in terms of the city climate and general living conditions. The initial idea was focused on suggestions concerning urban heat including infrastructure such as shade structures (tents), trees, water points and green facades. However, the concept of suggestions to improve the neigborhood can easily and intuitivly be expanded to include suggestions about social and ecological topics in general.

The application is map based, where users can add suggestions, represented by different icons, to a specific location and be able to see and participate in active suggestions in their neighborhood. A mockup has been created and can be seen in the header slides.

The application has the goal to facilitate active discussion between the users around the presented ideas and to enable direct action to be taken.

For that purpose, users can rate suggestions and comment critically or constructively. Users can decide to support a certain suggestions, which means they will actively collaborate and work for that suggestion to be further developed and implemented. A chat function between supporters could facilitate the organization and would be the first point of contact for new supporters.

One crucial part of implementing a suggestion is to convince the owners of the respective land (private or public) or at least get their permission. However ownership is often opaque, which is why we strive to integrate features that allow the users to quickly identify the owners of the respective piece of land and contact them. This is a non trivial problem due to data availability and concerns of data privacy. Our approach at the moment is to work with the API of the Swiss Katasteramt. However there information is only available about the responsible land register offices which mostly only give ownership information after personal request. Contact information could be provided to supporters or a contact form could potentially be implemented.

This application is similar to "Züri wie neu" (mySociety's popular map-based reporting platform), but includes proposals in/for urban spaces. It must be) privacy preserving and is (will be) implemented based on open source software and open data, in particular OpenStreetMap data as well as Open Government Data from the City of Zurich and SwissTopo.

For more info about the challenge visit https://etherpad.wikimedia.org/p/CoolUrbanSpaces For more info about data and services on OpenStreetMap (OSM) see https://etherpad.wikimedia.org/p/OSM

GitHub Repository for Backend Code: https://github.com/lbuchli/cool_urban_spaces_backend

Final Project Presentation: Google Slides

Local Sustainability Booster

What are the key factors influencing sustainable development at a community level leading to more PV, electric cars and renewable heating?


~ PITCH ~

Challenge

To identify the most important factors influencing sustainable development at the community level, we enriched the Energy Reporter (https://ory.short.gy/energiereporter) dataset with additional data. Thus, using different statistical and machine-learning methods, we can show how the expansion of PV, sustainable heating and electric mobility depends on different socio-demographic factors, on the existing infrastructure, on the weather and on the electricity price.

Presentation

https://ory.short.gy/02presentation

Data science

https://github.com/DangerousDyl/local_sustainability_booster

**Watch video of our presentation:**

NEST - a Data-driven Building Model

Predictive building modelling to improve building operation.


~ PITCH ~

Based on this challenge:

https://hack.opendata.ch/project/672

See Readme above for details.

**Watch video of our presentation:**

~ README ~

04 - NEST, a Data-driven Building Model

Summary

Predictive building modelling to improve building operation.

The Goal

Energy consumption and CO2 emmissions of buildings could be reduced significantly by improving the controlling of the heating and cooling systems. This must be done predictively due to the thermal inertia. Hence, the control requires an accurate prediction model.

The Challenge

The Empa team proposed this challenge (https://hack.opendata.ch/project/672) for the ENERGY & CLIMATE HACK 2021. Starting point was a research project on data predictive control. The proposed challenge was to improve the statistical modelling of the correlations between building controls and resulting room temperatures.

We have picked the data set for the SolAce unit (https://info.nestcollaboration.ch/wikipediapublic/building/solace/)

The Idea

We train a machine-learning algorithm to maintain a target temperature. The system is set up as two stages:

  1. Feature selection using the SULOV algorithm in Featurewiz.
  2. Use genetic programming in TPOT to automatically explore thousands of models.

Featurewiz

Select the model features. We use Featurewiz (an open-source python package) for automatically creating and selecting important features in the dataset that will create the best model with higher performance. Featurewiz uses the SULOV algorithm and Recursive XGBoost to reduce features to select the best features for the model. It also allows us to use advanced feature engineering strategies to create new features.

Featurewiz uses the SULOV (Searching for Uncorrelated List of Variables) algorithm. The algorithm works in the following steps.

  1. First step: find all the pairs of highly correlated variables exceeding a correlation threshold (say absolute(0.8)).
  2. Second step: find their Mutual Information Score to the target variable. Mutual Information Score is a non-parametric scoring method. So it's suitable for all kinds of variables and target.
  3. Third step: take each pair of correlated variables, then knock off the one with the lower Mutual Information Score.
  4. Final step: Collect the ones with the highest Information scores and least correlation with each other.

TPOT

We use the TPOT (Tree-based Pipeline Optimization Tool) Python library for automated machine learning. TPOT uses a tree-based structure to represent a model pipeline for a predictive modeling problem, including data preparation and modeling algorithms, and model hyperparameters.

TPOT optimizes machine learning pipelines using genetic programming. It explores thousands of possible pipelines to find the best one for the data. Once TPOT is finished searching, it provides Pythons code for the best pipeline it found so we can tinker with the pipeline from there.

What we did

The model was trained using Jupyter notebooks on Google Collab on a subset of the data in order to get results in a reasonable time.

The prototype

Featurewiz selected 11 optimal features:

Title

TPOT chose the XGBRegressor moddel as the best pipeline.

Resources/ Data

We have been using the SolAce Energy Demand and User Behaviour Data (https://figshare.com/articles/dataset/NEST_-_SolAce_Energy_Demand_and_User_Behaviour_Data/14376950). It contains 18 measurement points with a temporal resolution of 1 minute over the period of one year (July 2019 to July 2020).

The room temperature is a result of heat inflows and outflows of the unit. Heat inflows are

  • Space heating delivered to the unit.
  • Radiation that is coming through the windows.
  • Heat generated by the people present in the room. Heat outflows are
  • Heat that is transfered through the walls and windows to the outside world.
  • Space cooling delivered to the unit.

There are energy meters for the space heating and cooling delivered to the unit. We engineered the following features:

  • temp_room = average of temp_meeting and temp_office
  • rad_room = irrad * (blinds_height_F1 + blinds_height_F2 + blinds_height_F3 + blinds_height_F4) during 11am and 5pm and 0 outside of these hours
  • praes_room = maximum of praes_meeting and praes_office
  • temp_diff = temp_amb - temp_room

In addition, we have added the following time-based variables to help the algorithm deal with time-dependent phenomena:

  • day_of_week = 0 for Monday to 6 for Sunday
  • is_weekend = true for Saturday and Sunday
  • hour = hour of time variable

Next Steps

The is a number of next steps that should be taken from here

  • Due to time constraints, the current models were created with only a subset of the available data. With more time available, the training should be re-run with the entire data sets or with even larger data sets containing multiple years.
  • Specially constructured additional features can often improve a models performance. A few features have been created as part of this project. A possible next step should explore even more features.
  • In addition to the statistical analysis, the same data could be further analyzed through visualization. In particular, it would be interesting to investigate the temporal delays of the change in indoor temperature caused by energy input subject to the thermal inertia. The challenge submission suggested to look at H-scatterplots and cross-correlograms as tools to understand lagged correlations.
  • Finally, in order to establish trust in the results of the statistical model, model predictions should be validated through various analyses. The visualizations from the previous step could be adequate tools in order to understand how the predictions differ from actual measurements and, more importantly, how novel control algorithms reduce energy consumption while maintaining the same level of comfort.

Vertical Farms

Could vertical farms and Circular Food Systems become a part of the sustainability of the decarbonized city?


~ PITCH ~

Vertical farms have attracted great interest in recent years. This is not surprising, as they address many of the problems of conventional agriculture. They save land, can shorten the logistical distances of food transport and the plants grow in a controlled environment and thus do not need pesticides, are some examples. However, a clear disadvantage of vertical farms is the high electricity consumption. In this challenge, we want to find out how big the potential of vertical farms is and identify relevant criteria for the locations of such farms.

Presented by https://www.yasai.ch

Challenge slides

Challenge a):

Is the complete supply of a city like Bern possible through vertical farms? What are the most important restrictions for the complete supply through vertical gardening? Where is a clear advantage to conventional agriculture? Which are the advantages and disadvantages in terms of CO2 emissions, electricity and water consumption?

Challenge b):

Identify (and illustrate) ideal locations for vertical farms considering:

  • Local electricity costs
  • District heating feed-in potential
  • Local water price
  • Proximity to urban centres
  • Proximity to regenerative plants
  • Other factors?

Data

What we did:

Discussion of possible factors

1. Electricity

1.1 Electricity Costs

While for small consumers with a consumption of up do 100'000 kWh per year the costs for electricity is regulated by public tariffs and the supplier is determined by the consumer's location, larger consumers can freely choose any supplier and negotiate the costs with them [1]. Vertical farms typically are larger consumers, so the tariffs as published under [2] don't apply to them and their electricity costs don't depend on their location.

1.2 Proximity to regenerative power plants

This factor was initially proposed in the challenge pitch. But as vertical farms as large consumers don't need to choose a local supplier for their electricity, we doubt that the immediate proximity of regenerative power plants could be of much relevance. In case they still should be, various data on regenerative power plants in Switzerland would be available on [3].

1.3 Solar Energy Potential

The more of its electricity needed a vertical farm can produce by itself, the greater will be the cost advantage. which makes a location's solar energy potential an important factor to consider. Data about solar energy potentials by municipality, which is also being used for the platform www.sonnendach.ch, is publicly available on [4].

2. Transportation

2.1 Proximity to urban centers

Our initial assumption that a vertical farm should be as close to urban centers as possible to save transportation costs has proven itself wrong in the course of the project, as a vertical farm typically won't sell its produce directly to end consumers, but first will transport it to logistic distribution center, from where it will be further distributed to shops and supermarkets. So the proximity to a distribution center is far more important as a cost-saving factor. Besides, "urban center" is a very vague term, so no public and generally valid list of them and/or their locations is available.

Furthermore, urban centers are known for high rent costs, which actually would be a negative cost-factor.

2.2 Proximity to distribution centers

Transportation time and cost can be reduced by choosing a location as close as possible to a distribution center (cf. 2.1). While there is no published list, for this project we manually put together a list of the main distribution centers of the two large Swiss supermarket chains Coop and Migros. The information necessary for this we could retreive from the supermarkets' and their distribution centers' webpages as well as Google Maps, which provided us not only with the address but also the map coordinates of their location. This can be used to calculate and optimize the proximity of a vertical farms to those distribution center locations.

3. Rent cost

Rent cost is a crucial cost-factor to be considered when choosing a location for a vertical farm. However, we've found it difficult to find complete and reliable information that allowed us to compared rent costs in different regions of Switzerland.

One idea we've came up with is to use a location's population density as a possible indicator for how high the rent cost ist expected to be, parting from the assumption that in densely populated areas, the higher demand for objects to rend will also lead to higher rent costs.

4. Labour cost

A vertical farm can save on labour-cost by being located in an area with a lower wage-level. The most complete and up-to-date data about different wage-levels is the gross monthly wage (middle value) per greater region in Switzerland from the year 2018, published by the Federal Statistics Office [5].

5. Income Taxes

In Switzerland, income taxes can differ highly between different municipalities. So a location's income tax can become a possible relevant factor. We didn't do any more research on this factor in the course of this challenge but assume with confidence that the necessary data would be publicly available.

6. Proximity to district heating sources

Vertical farms produce a lot of heat, that need to be transported out of the building. It would be an advantage to use this heat for district-heating, so the proximity to district heating sources is a factor to consider. [6]

7. Building regulations

Vertical farm buildings need to be of a certain minimal height, which might not comply with every location's building restrictions and zone planning. Further research would be needed to determine possible datasources and their use for optimizing the choice of location.

Concept of an interactive map

To illustrate the advantages and disadvantages of different locations in Switzerland, as well as to help with the decision process to find a suitable location for a vertical farm, we came up with the concept of an interactive map of Switzerland. The users should be able to filter for those factors they themselves consider most relevant, and adjust the weight of each factor for calculating a final score.

Next steps?

  • Most of the available data sources look quite promising and useful, but more effort has to be put into cleaning and aggregating them.
  • So far, the interactive map only exists as a concept. The next step would be to implement a working interactive prototype.

**Watch video of our presentation:**

#Sources: [1] https://www.strom.ch/de/energiewissen/produktion-und-handel/strompreise [2] https://www.strompreis.elcom.admin.ch/ [3] https://www.uvek-gis.admin.ch/BFE/storymaps/EE_Elektrizitaetsproduktionsanlagen/ [4] https://opendata.swiss/en/dataset/solarenergiepotenziale-der-schweizer-gemeinden [5] https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.14127366.html [6] https://opendata.swiss/de/dataset/thermische-netze-nahwarme-fernwarme-fernkalte

Wärmcity Bern

Exploring the spatial pattern of heatwaves in the city


~ PITCH ~

Based on challenge #06:

https://hack.opendata.ch/project/674

In this project we created a Data Package and basic data science environment to visualize the locations of sensors, create a heat map using open source tools, and begin to address the more technical part of the challenge: Develop a transfer function ... to correct for the estimated radiative bias of each station of the monitoring network.

https://raw.githubusercontent.com/matthme/heat-spots-berne/main/datapackage.json

The Data Package contains data from the three heatwaves that was published by the researchers, plus additional specifications of the locations of the sensors, and meterological observations.

We have collected useful references and sketched a concept application that could be built with this data - see Project Notes.

👌 Results 💁

  • We have collected are lots of links to similar data and applications around the world, and sketched a basic design for an app inspired by our research into related projects. You can find these links and sketch in our Slack.
  • There is a basic suggested set up for doing open data science in Python to explore this data further. A basic Jupyter notebook (nbviewer preview) was started for further work in exploring the data. Please see README above for instructions on getting the code running.
  • A Data Package can be used for sharing this data further with the scientific and citizen scientist community. This is the basis of the GitHub repository (Source link below)
  • We collected some data of our own in Bern during the hackathon using the Logair.io open hardware sensor, just to have as a basis for comparison in regards to the data model.

**Watch video of our presentation:**

🚧 Under construction 🚧

  • Determine the roadmap and put together a more specific concept for this application.
  • Work out the design into a workframe that makes this data accessible and interesting to lay users.
  • Upload and compare data from the LogAir mobile sensor.
  • Discuss further Data Management needs with research groups on this topic.

🎍 Contributions welcome 😍

  • You are welcome to connect via GitHub (Source) and Slack (Contact) if you would like to continue this project.
~ README ~

Binder

Wärmcity Bern

We are trying to reproduce and interpret research of climate scientists into mapping and modelling of heat spots, or urban heat islands in the city using DIY (low cost and accessible-build) temperature sensors.

This is a project started at the #EnergyClimateHack hackathon on August 31, 2021. For more background visit the challenge page here, and contact us via GitHub issues or the Slack channel if you have questions.

See also NOTES.md for a sketch of our app idea, additional links and references.

Data

This repository contains data collected during three heat waves in Berne, Switzerland, during June 2018, June 2019, and July 2019.

The dataset was created and published by the Climatology Group of the Institute of Geography (Oeschger Centre for Climate Change Research, University of Bern), available as open access at Burger, Gubler, Heinimann, Brönnimann 2021.

We made an effort to "harmonize" the timestamp fields in the data, i.e. put them all in the same UTC format (some of them were only with AM/PM or vorm./nachm. indication). Both the original (1-s2*.csv) and adjusted (data_harmonized*.csv) data is in the data subfolder, along with the locations of the sensors (Metadata_Logger).

This is complemented by meteorological variables (Radiation, Precipitation, Wind speed, Wind direction & Temperature) from the official measurement station in Zollikofen during the same three heatwaves. See Met_Zoll*.csv. Caution! During hw18 wind speed was measured in m/s, during hw191 and hw192 it was measured in km/h.

We have ensured all data is in UTF-8 and a Simple Data Format (CSV), and created a Data Package containing all the readings and control measures. This can be validated and explored with Frictionless Data tools.

Explore

This repository includes a Jupyter data science notebook which you can preview here (nbviewer) to facilitate the sharing and further analysis and development using the latest open data sharing standards.

To start a cloud-hosted notebook that you can run and modify, use Binder or Colab.

Slightly slower start

To get this running on your local machine, we suggest:

  1. Install Anaconda and open a shell in the root folder of this project.
  2. Create a new environment using conda env create -n heatspots -f=environment.yml
  3. Run the command jupyter notebook to start Jupyter

You will see an initial Heatmap visualization. Click "Run all".

License

This project is open source using the MIT License

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