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).


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

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Ecosystem Governance in the context of a blockchain network

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The creation of non-zero-sum games is not an easy task. Would you like to know more? Play the game Link

Watch video of our presentation:

Resources / Data:

[1] What is a stakeholder Analysis

[2] Flows of Value - The concept

[3] Elinor Ostrom's 8 Principles for Managing A Commmons

Next Steps:

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:

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:

Based on this challenge:

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

See Readme above for details.

Watch video of our presentation:

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

• Food consumption by type https://opendata.swiss/de/dataset/nahrungsmittelverbrauch-nach-art-der-nahrungsmittel4
• Development of food consumption: https://opendata.swiss/de/dataset/entwicklung-des-nahrungsmittelverbrauches-in-der-schweiz-je-kopf-und-jahr4
• Freight transport Road freight transport performance by commodity group, 2019: https://www.bfs.admin.ch/bfs/de/home/statistiken/mobilitaet-verkehr/gueterverkehr.assetdetail.1189-1900.html
• Electricity costs: https://www.strompreis.elcom.admin.ch/
• District heating feed-in: https://opendata.swiss/de/dataset/thermische-netze-nahwarme-fernwarme-fernkalte
• Proximity to regenerative power plants: https://www.uvek-gis.admin.ch/BFE/storymaps/EE_Elektrizitaetsproduktionsanlagen/

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

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.

Projects based on this challenge:

https://hack.opendata.ch/project/765

Although buildings account for almost half of Switzerland's total final energy consumption, digitalisation in the building sector is not yet very advanced. At NEST, Empa's demonstrator, new technologies and algorithms are being developed and tested. For this purpose, NEST is monitored in detail by some 10,000 sensors. The aim of this challenge is to use the rich measurement data from the NEST to give buildings the opportunity to look a little into the future and thereby take an important step towards digitalisation and energy efficiency.

(DE Zusammenfassung)

NEST Fallstudie für "Decarbonising Cities" - Kostengünstiges, daten-getriebenes Gebäudemodell.

Obwohl Gebäude fast die Hälfte des gesamten Schweizerischen Endenergieverbrauchs verursachen, haben die Digitalisierung und ihre Versprechen hier bisher kaum Einzug gehalten. In dem Empa Demonstrator NEST, werden neue Technologien und Algorithmen entwickelt und getestet. Für diesen Zweck wird das NEST bis ins Detail sensorisch überwacht. Ziel dieser Challenge ist es, die Messdaten des NEST zu nutzen, um Gebäuden die Möglichkeit zu geben, ein wenig in die Zukunft zu blicken und dadurch einen wichtigen Schritt in Richtung Digitalisierung und Energieeffizienz zu machen.

Motivation

Buildings are responsible for 60% of the energy consumption and 40% of the C02 emissions in Switzerland (overall energy statistics SFOE). Through optimal operation, i.e. without structural changes, consumption can be reduced by an estimated 20%.

However, the optimal operation of a building is a methodological challenge. Due to the thermal inertia, regulating measures (e.g. flow rate of the floor heating, charging of a storage tank) must be carried out predictively. Other influencing variables, such as solar radiation, however, have a direct influence on the indoor climate and must therefore be taken from predictions at the time of the regulating intervention.

Building models are used to solve this methodological challenge algorithmically. Building models can either be created deterministically on the basis of physical laws (model predictive control) or they can be learned from measurement data. The NEST research building of Empa and EAWAG grants access to measurement data from several buildings with different types of uses. Measurement data for all relevant systems and building functions have been stored for several years with a temporal resolution of one minute. The data situation, for example for creating data-driven building models, is unique.

Goal

The overall objective is to investigate the dependencies between the current energy input, indoor climate and weather, and the indoor climate at a later point in time. The corresponding question is: Which energy input leads to which future indoor climate for a given initial situation (current indoor climate, weather)?

Participants can choose their own approach or they can use the following, more detailed objectives and questions as a guide.

1) Visualisation of the interrelationships:

• Which energy input leads to which indoor temperature?
• How delayed is the reaction of the indoor temperature to a change in the energy input (i.e. how great is the thermal inertia)?
• What influence do boundary conditions such as the outside temperature or the current indoor climate have on the subsequent indoor climate?
• Do the above relationships vary between different rooms? Methodological note: H-scatterplots or cross-correlograms, among others, are suitable for the explorative investigation of "lagged correlations".

2) Statistical modelling of the correlations from 1):

• Can these correlations be quantified with a statistical model?
• Do the correlations become clearer if not only the current measurements are used as predictors, but also the last N historical measurements?

3) Sensors are expensive. In the real situation, there are usually fewer sensors available:

• Which sensors (predictors) have which significance in the model from 2)?
• Can the model be reduced by individual sensors to save costs without the prediction of future room temperature suffering greatly?

Data availability

The participants are provided with the data from the scientific study in an adjusted form, as CSV.

• An overview of the measurement data and infrastructure at NEST is available at the following link: https://info.nestcollaboration.ch/wikipediapublic/.
• The measurement data of the NEST research building can be accessed via REST API.
• A detailed description of the sensors (incl. costs) is provided.
• a Graphana dashboard provides visual access to all relevant measurement data.
• Additional measurement data, such as outdoor temperature or solar radiation, can be obtained via publicly accessible interfaces.
• Literature: Bünning, F., Huber, B., Heer, P., Aboudonia, A. and Lygeros, J., 2020. Experimental demonstration of data predictive control for energy optimisation and thermal comfort in buildings. Energy and Buildings, 211, p.109792.
• More info: https://www.empa.ch/de/web/nest/

In principle, the above goals can be pursued for several units at NEST. However, it makes sense to start with the Urban Mining & Recycling (UMAR) unit. The measurement data from UMAR were recently used in a research project for a similar purpose (Bünning et al. 2020). Each unit is based on a specific thesis. In UMAR, this is that all resources needed to produce a building must be fully reusable, recyclable or compostable.

Left: UMAR from the outside. On the left and right in the picture are the two bedrooms. Right: Floor plan of the unit. Above left and right the two bedrooms, in the middle the living room. UMAR offers living space for two guest researchers or students. The unit has a living room with kitchen, two identical bedrooms and two bathrooms. Detailed measurements are available for the consumption of hot and cold water, electrical energy and heating and cooling (details here). With this information, a statistical model can be created and trained, which can estimate the expected room temperature based on the actual state (e.g. room temperature), a measure (e.g. increase heating output) and the weather forecast for tomorrow.

Project started on this challenge:

https://hack.opendata.ch/project/762

Summary

Since summer 2018, the Climatology Group of the Institute of Geography (University of Bern) measured air temperatures at 10 minutes intervals using 85 low-cost sensors placed in a radiation shield across the city of Bern. Due to the passive ventilation of the radiation shield, a radiative error is likely to occur during periods of high solar irradiance and low wind speeds. In order to assess the magnitude of this radiative bias, reference measurements at three professional weather stations were performed. However, we still do not know how large the error is at the remaining stations of the monitoring network. The goal of this challenge would thus be to develop a transfer function based on biases and meteorological parameters measured at the three reference stations, as well as land-cover data and building geometry data in order to correct for the estimated radiative bias of each station of the monitoring network.

The challenge

The goal of this challenge would thus be to develop a transfer function based on biases and meteorological parameters measured at the three reference stations, as well as land-cover data and building geometry data in order to correct for the estimated radiative bias of each station of the monitoring network.

Link to the project

https://www.geography.unibe.ch/research/climatology_group/research_projects/urban_climate_bern/index_eng.html

Data Sources

• Raw temperature data of all stations of the monitoring network
• Meteorological parameters measured at the three reference stations
• Land-cover data
• Building geometry data

Project based on this challenge:

https://hack.opendata.ch/project/764

Background

Cities emit 75 % of the worldwide energy-related CO2-Emissions. More than 65 % of the wolrd’s population will live in cities by 2050. 80% of the global GDP is produced in cities and is at risk [1].

The Problem

Globally 1.1tn USD are needed annually to finance green urban infrastructure [1]. Switzerland needs annually 12.9 bn CHF to achieve its net zero target by 2050 [2].

From the global perspective, three main problems have been identified. The "funding gap", the "transparency gap" and the"efficiency gap [3]. On the level of the city the main problem is limited access to finance. The main reasons are low creditworthiness of cities, few bankable projects, difficult access to existing financing and limited climate finance knowledge [4]. Problematic is not only the amount of money available for urban renewable energy projects. We need to be able to mobilise private and public funding and channel alternative sources as well and make it accessible. To achieve this, we need to create an infrastructure and a framework that facilitates existing financing instruments and integrates innovative or alternative ones form different sources.

The Solution

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 (DeFi) 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. By Utilising blockchain technology and implement DeFi concepts we could create innovative, scalable and replicable financing solutions and reward mechanisms to transform our cities to sustainable smart-cities.

The Challenges

Challenge 1

Build a MVP for lending, borrowing and auctioning digital assets, that allows PV systems owners to (re)finance existing or new projects, and trading ownership rights of their projects. Implement the concept of fractional ownership, tokenisation of energy and utilisation of open-source blockchain and DeFi protocols.

Challenge 2

Create reward/incentive mechanisms for renewable energy production, provide flexibility, for sharing energy related data or work on your own project.

Watch the video of the presentation:

References:

[1] Renewable Energy in Cities Status Report 2021

[2] Investment and financing needed for Switzerland to reach net-zero by 2050

[3] Transforming Climate Finance and Green Investment with Blockchain Technology, 2018

[4] C40 announcement at Global Climate Action Summit, 2018

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