Active projects and challenges as of 21.11.2024 12:35.
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lofos
#makeopendata
Electric mobility makes it easy and fun to get from point A to point B, but our range is limited by battery capacities. Personal mobility devices use lightweight batteries and need regular charging. Users of such devices have to rely on the availability of power outlets along the way to further destinations.
At the Swiss Open Energy Data Hackdays in April 2016 we sketched an app, prototyped a basic idea, and proposed some open/crowdsourced datasets that could allow users of personal mobility new levels of freedom of movement.
Lófos
Electric mobility makes it easy and fun to get from point A to point B, but our range is limited by battery capacities. Personal mobility devices use lightweight batteries and need regular charging. Users of such devices have to rely on the availability of power outlets along the way to further destinations.
At the Swiss Open Energy Data Hackdays in April 2016 we sketched an app, prototyped a basic idea, and proposed some open/crowdsourced datasets that could allow users of personal mobility new levels of freedom of movement.
The prototype is made using Ionic Framework, Raphaël and Google Maps Elevation API.
Demo: http://soda.camp/workshops/2016/lofos
Next data sources
Our next steps are plug in open data sources and/or create a crowdsourced databases to collect the following information:
Elevation data
Currently we use Google Maps API to obtain accurate elevation profiles of streets. We would welcome suggestions of other sources of such information. Users of apps like Ride With GPS help to create accurate GPS profiles, but this data is not shared with third parties.
Electric Vehicle battery data
While there are plenty of individual community projects (e.g. Segway Battery FAQ), there does not seem to be any wider effort underway to collect specifications about the power characteristics of personal mobility. Information such as the different ranges, top speeds, drive system (motor) power (wattage), maximum inclines, could be compiled and cross-referenced.
Public concerns about "electronic mobility aids" catching fire in transport that have led to bans on most flight carriers could also be potentially mitigated or better discussed publicly with access to comprehensive and accurate information about their engineering.
Public electric sockets
PlugShare ("the world's largest electric vehicle (EV) charging network with a database of 50,000+ charging stations" -FAQ) connects EV users with charging locations. According to data.gov, much of the data is sourced in the USA from the Energy Department's Application Programming Interface (API) for the Alternative Fuels Data Center.
In Europe, the non-profit association LEMnet.org collects and distributes this kind of data. We only discovered them after the hackathon and will look into using it next.
Traffic
Construction sites and other obstacles to personal mobility, status of traffic lights, special lanes for electric vehicles - there is a world of data possibilities in this area. The Opendata.ch Transport Working Group in Switzerland and similar efforts worldwide are opening data sources to enrich applications like this one. For example, see Geneve Velo, another hackathon project.
Next interfaces
We are fascinated by data visualisation in Virtual/Augmented/3D/Printed/QR/NFC and other "tangible" contexts, and are keen to explore interfaces for our application that allow viewing such information on the go. Here are some inspiring commercial and research projects which make this connection of personal mobility and new data sources and new data interfaces: Daqri, RideOnVision, Catapult (dailydot), In-place Augmented Reality (researchgate.net)
Team
Trivia
Sidenote: the name of the project comes from the Greek λόφος, meaning 'hills'. We are inspired by Odysseus and his hilly birthplace Ithaka.
by Edward Dodwell, Public Domain.
Swiss Energy Balance
The goal of this project is to evaluate the cost of energy transactions between Cantons comparing their energy consumption and production. We want to simulate the economical impact related to the geographical distribution of production vs consumption.
We already found a database containing production/consumption values per Cantons and we are in the process of creating the visualisation
Unfortunately,we only found aggregated production data and we would like to differentiate the sources of production, so if anyone has any ideas/data please let us know!
We are also looking for ideas on how to model the price of infrastructure related to geographies of production and consumption. Our first idea would be to set a coefficient dependant on the geographical distance between the centres of each canton.
Challenges
Atum
Atum is a web application for energy production forcasts for photovoltaics (pv). User can register their own pv sites and specify its location among other parameters. Atum then calculates and displays the expected energy production for the upcomming days.
Data
- meteoblue offers us access to their API which provides us with the expected amount of sunshine by any location worldwide
Team
- psunix & Puzzle ITC members
- and other team members
Links
Hot or not
Energy customer profiling made easy
How is hot for putting a next solar power system of his roof?
As a service provider I want to
Potential customers: -Energy service provider -Utilities -Public institutions such as Cantons and Municipalities -Planning and engineering companies / product developers
Service: -Generation of individual persona (customers of our customer) profiles based on social economic and social demographic data
Data
- List and link your actual and ideal data sources.
Team
- and other team members
Klumpenrisiko
We are on an insane mission to find the real owners of the 10 biggest water power plants in Switzerland.
The ultimate goal of this project is a visualization that brings real ownership transparency to a complex and often intransparent economic sector.
In the end, we'd like to show which cantons and municipalities own what parts of power companies - and how they would be affected by a bankruptcy of the latter.
Methodology
- We take the water power plants as roots and go forth: Let's take Innertkirchen I as example
- Who maintain it? KWO
- Who owns the maintainer? BKW, EWB, etc.
- Who owns the owner of the maintainer? City of Berne, Canton of Berne, etc etc etc.
- etc.
Data
- All collected by ourselves from company websites and Wikipedia etc.
Team
- @grssnbchr
- @davidoesch
- @mhertach
- @philshem
- @HaRii66666
- @gluckstaler
- @satuk34
- @mikeschudel
- @scruggia
Sideprojects
An awesome map by @davidoesch showing municipal and regional dependency on power suppliers.
Periodic aggregation of electricity system data
This project is about using the web scraping platform morph.io to periodically download, clean up and aggregate data about the European electricity system. At the moment, we focus on time series data about renewables in-feed and electricity demand.
Scripts to download and process data about the electricity system are already available from a platform called Open Power System Data (http://open-power-system-data.org/). The project http://open-power-system-data.org/ aggregates and processes data about the European electricity system and publishes these as clean CSV files. The platform is still in beta and about to launch publically in autumn 2016. It has published its data processing scripts already on Github (https://github.com/Open-Power-System-Data), which is what we are using as a base for the morph.io scraper we are developing in this make.opendata.ch project.
The scraper (work in progress): https://morph.io/metaodi/open-power-system-timeseries
Data
- Transmission system operators: ENTSO-E, TransnetBW, TenneT, 50 Hertz
Team
Links
- Relevant documentation …
- Blog or forum posts …
- Tools you used …
Swiss Energy Dashboard
We are working on a toolkit / demo application where we try to visualize data coming from an OpenHAB setup. This setup sends some key values like electricity consumption and production. We are taking this information and putting it into an ElasticSearch cluster using MQTT, Xively and Logstash, and enrich the data with PLUS Energy building database from energy-cluster.ch and eco-villages.ch
The key thing we're trying to do is calculate PLUS Energy factor of the home - basically a number which says if it is energy-neutral/-friendly/-negative. We try to correlate this data, get a simple number, and create a dashboard visualizing these numbers.
We need more data! If you have data from your house, please send it - format specification: 16_Database-1000_peg_Template.xls
Data
- Self-produced
Team
- Didier
- Hannes
- Olaf