{"contributors":[],"created":"2026-09-07T20:25","description":"Come and join us for the 5th edition of the Energy Data Hackdays at the FHNW in Brugg and work as a team on the future of energy!","homepage":"","keywords":["EDH23"],"licenses":[{"name":"ODC-PDDL-1.0","path":"http://opendatacommons.org/licenses/pddl/","title":"Open Data Commons Public Domain Dedication & License 1.0"}],"name":"event-1","resources":[{"data":[{"aftersubmit":"","boilerplate":"### 🚀 Let's launch your idea!\r\n\r\nWrite a **Title** and short **Summary**, select a **Template** if one is available, or use the **Readme** link to fetch an open source repository on [GitHub](https://github.com), [GitLab](https://gitlab.com) or [Bitbucket](https://bitbucket.org); an online document at [Etherpad](http://etherpad.org), [Instructables](http://instructables.com), [HackMD or CodiMD](https://hackmd.io), [Google Docs](http://docs.google.com) (Published to Web) or [DokuWiki](http://make.opendata.ch/wiki/project:home).\r\n\r\n_Need more help?_ Get in touch with the organising team, or raise [an issue](https://github.com/dribdat/dribdat/issues).\r\n","certificate_path":"","community_embed":"
All attendees, sponsors, partners, volunteers and staff at our hackathon are required to agree with the Hack Code of Conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to ensure a safe environment for everybody.
\r\n\r\n

\"CreativeThe contents of this website, unless otherwise stated, are licensed under a Creative Commons Attribution 4.0 International License.

\r\n\r\n\r\n
","community_url":"","custom_css":"","description":"Fundamental changes are on the horizon in the energy sector. The end of the service life of nuclear power plants calls for the development of diverse, regenerative and decentralized energy technologies, such as geothermal energy, wind and solar technology. Digital tools are being used in more and more areas in order to optimize and stabilize the interplay between energy supply, use and storage.\r\n\r\n
Resources
","ends_at":"2023-09-16T16:00","gallery_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/1/IX85B5SVH3W7T4T2J1BAPPX8/IMG_20230915_095056_1.jpg","has_finished":true,"has_started":false,"hashtags":"#EDH23","hostname":"Hightechzentrum Aargau, HSLU, Opendata.ch ","id":1,"instruction":"\r\n\r\n\r\n\r\n\r\n\r\n
\r\n\r\n
\ud83d\udd0b Open Energy Data
\r\n\r\nHere are diverse open energy datasets that we have researched and prepared for the Hackdays. Additional suggestions and requests can be submitted here or via GitHub.
\r\nNew, compiled by the SFOE and sorted along the value chain, this collection of open CH related datasets
\r\nOn data portals like opendata.swiss you can discover more open datasets such as these. Make sure to follow the Terms of Use and correctly attribute your open data sources.\r\n\r\n\r\n","location":"FHNW Brugg-Windisch","location_lat":0.0,"location_lon":0.0,"logo_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/public/163/71ZMFIEKTROU65J0ZS3G066V/energy_data_hackdayss.jpg","name":"Energy Data Hackdays 2023","starts_at":"2023-09-15T09:00","summary":"Come and join us for the 5th edition of the Energy Data Hackdays at the FHNW in Brugg and work as a team on the future of energy!","webpage_url":""}],"name":"events"},{"data":[{"autotext":"# edh2023\nThanks for choosing Axpo's challenge, **Predicting Voltages in Substations**, for the Energy Data Hackdays 2023. We're glad you're here!\n\n## Getting Started\n\nThis README will guide you through the process of setting up your environment and working on the challenge. By the end of this guide, you'll be ready to dive into the provided datasets, create your predictive model, and help us improve grid stabilization.\n\n### Prerequisites\n\nBefore you begin, please ensure you have the following prerequisites:\n\n1. **Access to the VM with Jupyter Hub:** We've provided a virtual machine with Jupyter Hub installed. This will serve as your development environment. Just go to our [Jupyter Hub](http://axe-lab-appl-energy-data-hackdays.westeurope.cloudapp.azure.com/hub) and sign in with a username (no special characters or spaces) and password of your choosing - just don't forget it.\n\nYou can also develop on your local machine if you prefer. Talk to us about getting the dataset onto your local machine.\n\n### Setting Up Your Environment\n\nFollow these steps to set up your environment and start working on the challenge:\n\n1. **Access Jupyter Hub:**\nOpen your web browser and navigate to the provided URL for Jupyter Hub. Log in using your credentials.\n\n2. **Clone the Git Repository:**\nOpen a terminal and clone this repo\n```console\ngit clone https://github.com/axpogroup/edh2023.git\ncd edh2023\n```\n\n3. **Accessing Datasets:**\nThe training and validation datasets for both substations are located in the `/data` directory on the Jupyter Hub VM. You can copy them to the data directory with \n```console\ncp /home/data/data.zip ~/edh2023/data.zip\nunzip data.zip\n```\n\n4. **Installing Dependencies:**\nCreate a virtual environment to install dependencies:\n```console\npython -m venv .venv\n```\nActivate the virtual environment\n```console\nsource .venv/bin/activate\n```\nTo ensure your environment has the necessary packages, run the following command:\n```console\npip install -r requirements.txt\n```\nRegister the virtual environment as ipykernel:\n```console\npython -m ipykernel install --user --name=edh_venv\n```\nIt will take a few seconds for the new environment to show up as an available. You can open the `sample_notebook.ipynb` and select the environment as kernel once it is and get going with digging into the details.\n### Understanding the Challenge\n\nBefore you start coding, it's important to grasp the problem at hand:\n\n- You are provided with time series of substation measurements, energy production, weather data, and target voltages for two substations with shunt reactors.\n\n- The goal is to create a model that can predict voltages in the substation such that, based on these predictions, one can decide when to turn the shunt reactors on or off to keep the measured voltage close to the target voltage. You should keep the average **number of on-off/off-on switches below 2/day** to limit wear and tear on system components. One of the main challenges herein is\n\n### Your Task\n\nYour main task is to develop a predictive model that predicts the voltage in each substation and that can effectively recommend when to activate or deactivate shunt reactors in order to stabilize the grid voltages. Use the provided datasets to train and validate your model. They contain weather, electricity production, and grid measurement data. Take a look at the `sample_notebook.ipynb` to get started with analyzing the data.\n\nFeel free to explore different machine learning algorithms, techniques, and preprocessing methods. Don't hesitate to innovate and experiment!\n\n### Evaluation\n\nYou can evaluate your model with either the root mean square error for the voltage prediction or `utils.eval.alternative_strategy_reward`. Both of these are relevant metrics for us.\n\n## Need Help?\n\nIf you encounter any issues during the challenge or have questions about the provided datasets, feel free to ask one of us for help. \n\nHappy coding!\n","autotext_url":"https://github.com/axpogroup/edh2023","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05SASXF7K4","created_at":"2023-08-28T09:56","download_url":"https://github.com/axpogroup/edh2023/releases","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"For grid stabilization, operators of different voltage levels have target voltage schedules. These support grid stabilization. It is possible to influence the voltage in a substation with shunt reactors via reactive power compensation. Turning these shunt reactors on or off causes transients, which in turn cause wear and tear on the components of the substation and the shunt reactor. This means a grid operator wants to minimize the number of on/off switches of the shunt reactor while still using...","hashtag":"","id":11,"ident":null,"image_url":"https://avatars.githubusercontent.com/u/98512946?v=4","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"For grid stabilization, operators of different voltage levels have target voltage schedules. These support grid stabilization. It is possible to influence the voltage in a substation with shunt reactors via reactive power compensation. Turning these shunt reactors on or off causes transients, which in turn cause wear and tear on the components of the substation and the shunt reactor. This means a grid operator wants to minimize the number of on/off switches of the shunt reactor while still using it to keep the voltage in the substation as close as possible to the target voltage published by the higher voltage level grid operator. We have time series measurements and target voltages for several of our substations with shunt reactors and aim to train a model that can tell us when to turn the shunt reactors on or off.\r\n\r\nChallenge Owner: **Axpo Grid AG**\r\n\r\n* Nicolas Pelzmann\r\n* Tobias Schmocker\r\n* Sandro Renggli","maintainer":"","name":"12: Predicting Voltages in Substations","phase":"Research","progress":10,"score":48,"source_url":"https://github.com/axpogroup/edh2023","stats":{"commits":1,"during":8,"people":11,"sizepitch":923,"sizetotal":4908,"total":17,"updates":5},"summary":"Prediction of Voltages in Substations for Grid Stabilisation","team":"adrian_buntschu, david, lara_b\u00e4rtschi, marco_steiner, ayam_babu, alison_fersch, throjma, pascal_cornu, jeffrey_honold, nicolas_dallo, nicolas_pelzmann","team_count":11,"updated_at":"2023-09-19T08:56","url":"https://new-hack.energy.opendata.ch/project/11","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/RTW6MRHP6ZL90SF0I06TUEL7/challenge_12_Energy_Data_Hackdays_Voltage_Prediction_results.pptx2komprimiert.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05RUBN4LKV","created_at":"2023-08-28T09:40","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Residential e-mobility charging is expected to support the energy transition for increasing the share of renewable energy. However, the potential is often estimated based only on simulations and without any real-world data. In this challenge, you can assess the flexibility potential with unidirectional and bidirectional charging of residential e-mobility charging based on real-world data. For this purpose, we bring lots of real-world load- and charging data from over 500 charging stations and ca...","hashtag":"","id":2,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"Residential e-mobility charging is expected to support the energy transition for increasing the share of renewable energy. However, the potential is often estimated based only on simulations and without any real-world data. In this challenge, you can assess the flexibility potential with unidirectional and bidirectional charging of residential e-mobility charging based on real-world data. For this purpose, we bring lots of real-world load- and charging data from over 500 charging stations and cars. What is the flexibility potential of this fleet of chargers and cars? How could this flexibility be utilized and how big are potential monetary benefits? How can flexibility and monetary benefits be increased by bidirectional charging compared to only unidirectional charging?\r\n\r\nEKZ: Ludger Leenders, Bendikt Hilpisch","maintainer":"","name":"01: Assessing flexibility potential based on real-world e-mobility data","phase":"Research","progress":10,"score":43,"source_url":"","stats":{"commits":0,"during":12,"people":9,"sizepitch":822,"sizetotal":902,"total":23,"updates":14},"summary":"Discovering the flexibility / peak shaving potential in residential EV charging.","team":"michele_bolla, fabian_luethard, jan_schlegel, manuel_meyer, giacomo_pareschi, mathias_steilen, winnie_chan, wolfram, Bill","team_count":9,"updated_at":"2023-09-19T08:42","url":"https://new-hack.energy.opendata.ch/project/2","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/C7H8NB9MA5RBU191GBMGZH0W/challenge_1_assessing_flexibility.pptx.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05RWA99M7Z","created_at":"2023-08-28T09:49","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Create a user-friendly tool to offer building owners personalised insights for early and informed energy planning decisions.\r\n\r\nOpen Data sources with APIs have been identified, as well as possible User Outputs and Web Design (Prototypes).\r\n\r\n* Define the Information that should be displayed to the users according to the available raw data\r\n* Define the logic necessary to go from the raw Open Data to the result information that will be displayed to the users\r\n* Prepare the data according to the ...","hashtag":"","id":4,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"Create a user-friendly tool to offer building owners personalised insights for early and informed energy planning decisions.\r\n\r\nOpen Data sources with APIs have been identified, as well as possible User Outputs and Web Design (Prototypes).\r\n\r\n* Define the Information that should be displayed to the users according to the available raw data\r\n* Define the logic necessary to go from the raw Open Data to the result information that will be displayed to the users\r\n* Prepare the data according to the defined logic to be used as a source table for the tool landing page\r\n* Web Design: analyse the already available prototypes and define how the user interface should look like for a user-friendly use of the platform\r\n* Customer Journey Design: this landing page provides information to the users about their building. It is the first step of the process. Define the Customer Journey the users should be guided through in order to go from the first expression of interest to making them actual clients\r\n* Web Development: Develop the Webapp. Available Tool: ArcGIS Enterprise Experience Builder (no-Code Tool). Available start code in Python, HTML, CSS. Any other Tool or Language however also possible.\r\n\r\nContext\r\nThe city of St.Gallen and the OST Fachhochschule have been working together in an Innosuisse project, that found out that a lot of building owners are overwhelmed with energy issues. There are usually not well informed and only act when they have to, for example when the heating system is breaking down. Without planning ahead, the replacing technology will often be a fossil heating system (unless a local legal prohibition has already come into force). To avoid this, we want to provide building owners with personalized information about their building.\r\n\r\nChallenge Miro Board with Data, APIs and Design Prototypes: [miro.com](https://miro.com/app/board/uXjVMvoVFIk=/?share_link_id=101430938955)\r\n\r\nRepo: https://gitlab.com/ssgw/\r\n\r\nLive Plattform: https://energyhackday.mobiparking.ch/\r\n\r\nChallenge Owner: SGSW\r\nClara Esteve, Ramon Schmid, Eren Baglar\r\n\r\n![EDH 2023.png](https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/7/AEV3XS93AL072C3UOWVMDEIR/EDH_2023.png)\r\n\r\n![2023091516 Energy Data Hackdays Building Energy Insights Vorgehen 1.jpg](https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/7/TITOHFD5NAZQU8VQ0AMR2XUU/2023091516_Energy_Data_Hackdays__Building_Energy_Insights__Vorgehen_1.jpg)","maintainer":"","name":"05: Building Energy Insights - Empowering Building Owners","phase":"Project","progress":5,"score":42,"source_url":"https://gitlab.com/ssgw/","stats":{"commits":0,"during":4,"people":16,"sizepitch":2495,"sizetotal":2620,"total":26,"updates":10},"summary":"Create a user-friendly tool to offer building owners personalised insights for early and informed energy planning decisions","team":"clara_esteve, aline_freitas, noel_oliveira, alex_wirz, patrice_blechschmidt, christoph_zemp, lukas_gysin, ramona_m, TobiasSigel, harry, matthias_sarbach, andre_eggli, sean_goff, felix_meier, marco_inniger, fabian_eichenberger","team_count":16,"updated_at":"2023-09-19T08:51","url":"https://new-hack.energy.opendata.ch/project/4","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/PFQSK9EH95ND3B8XH3FQDAW4/challenge5_building_energy_insights.pptx2.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05SDAC7CKW","created_at":"2023-08-28T09:54","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"This challenge is about forecasting the transmission losses on the Swiss transmission grid. More specifically, you will be asked to forecast the losses for the next day in hourly resolution. As the transmission system operator in Switzerland, Swissgrid is responsible to procure energy to compensate losses on the transmission grid. Part of the procurement is done one day before the real present time. Based on the day-ahead forecast, we can know how much to procure. More accurate forecasting will ...","hashtag":"","id":12,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"This challenge is about forecasting the transmission losses on the Swiss transmission grid. More specifically, you will be asked to forecast the losses for the next day in hourly resolution. As the transmission system operator in Switzerland, Swissgrid is responsible to procure energy to compensate losses on the transmission grid. Part of the procurement is done one day before the real present time. Based on the day-ahead forecast, we can know how much to procure. More accurate forecasting will help improve the procurement performance and therefore lower transmission system costs.\r\n\r\nIn this challenge, you\u2019ll practice your machine learning skills in a well-prepared VM data science environment with cleaned training datasets from the energy industry.\r\nYou will have access to GPU for your machine learning models so you can focus on modelling!\r\n\r\nThis is a great chance to explore different models and to improve your skills in forecasting.\r\n\r\nContext:\r\nThe active losses on the transmission grid can be influenced by many factors, such as the historical active losses, renewable generation, cross border flows between Switzerland and the neighboring countries, weather, etc. The raw data will be provided as time series data between 2019 and 2021, in hourly resolution.\r\n\r\nThe following datasets are provided for you to include in the model:\r\n\r\nHistorical active losses data\r\nSolar generation data for Germany and Italy\r\nWind generation data for Germany and Italy\r\nTemperature data for Switzerland, Germany, Italy, France\r\nNet Transfer Capacity (NTC) between Swissgrid and the neighboring TSOs (NTC is the maximum exchange programme between two areas which is consistent with the security standards of both areas)\r\n\r\nOutcome:\r\nThe well-performed models will be introduced to our internal forecasting process and support us make better decisions in our market operational tasks, and will eventually help us improvement procurement performance and reduce procurement costs. This is your chance to make a real impact!\r\n\r\nWe also have various experts from different parts of Swissgrid here so its also a chance to get to know us as an employeer.\r\n\r\nChallenge Owner: **Swissgrid**\r\nLiu Xiying, Tim Breitenbach, Giulio Ferraris","maintainer":"","name":"10: Day-ahead active losses forecasting","phase":"Research","progress":10,"score":41,"source_url":"","stats":{"commits":0,"during":10,"people":15,"sizepitch":2230,"sizetotal":2230,"total":22,"updates":7},"summary":"","team":"xiying, thomas_felder, adrian_rupp, patrick_schrmann, philipp_hillen, meinj, tobias_jucker, shajivan_satkurunathan, jorge_goncalves, jolivier, jerome_leveque, noe_duruz, tim_breitenbach, sandy_duruz, mathias_steilen","team_count":15,"updated_at":"2023-09-19T08:54","url":"https://new-hack.energy.opendata.ch/project/12","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/G4S6GT6X0PNP6836TVIKA6UB/challenge10_swissgrid_EDH_metrics.pptx.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05SARSEF34","created_at":"2023-08-28T09:47","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Energy suppliers will be faced with an increasingly complex production and consumption landscape. In order to anticipate these complex changes new methods for categorizing customers or novel services are needed. This challenge focuses on new methodologies to detect the flexibility potential of prosumers. As novel smart-home devices will allow automatic control of high-power consumption devices like EV-charging, electric heating (heat pumps), tumble dryers in private households or even complex ma...","hashtag":"","id":5,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"Energy suppliers will be faced with an increasingly complex production and consumption landscape. In order to anticipate these complex changes new methods for categorizing customers or novel services are needed. This challenge focuses on new methodologies to detect the flexibility potential of prosumers. As novel smart-home devices will allow automatic control of high-power consumption devices like EV-charging, electric heating (heat pumps), tumble dryers in private households or even complex machinery in industrial facilities \u2013 flexibility is driven by energy efficiency or financial incentives via dynamic tariffs. One key aspect will be the maximal load of a household or an industrial facility on the local grid. Flexible tariffs are designed to reduce peak loads and will be a financial incentive for scheduling consumption and production. This challenge aims to identify unknown devices as flexibility potential in unlabeled consumption data \u2013 paving the way for smart tariffs and novel services in the portfolio of a modern energy supplier.\r\n\r\nChallenge Owner: **Primeo Energie AG**","maintainer":"","name":"03: Beyond Labels","phase":"Research","progress":10,"score":35,"source_url":"https://colab.research.google.com/drive/1KdsIOs91fQ3QhN4YmfH9rum5lL2-yRVE?authuser=1#scrollTo=Z3cTGg6o2AXI","stats":{"commits":0,"during":11,"people":6,"sizepitch":1095,"sizetotal":1154,"total":14,"updates":8},"summary":"Uncovering Flexibility Potential in Energy Consumption Data","team":"orhan_yildirim, emilieboillat, andreas_schuth, tobias_graml, LarsKa, timo_kropp","team_count":6,"updated_at":"2023-09-19T08:47","url":"https://new-hack.energy.opendata.ch/project/5","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/1PQX7JCKK6VH4GNA8OKFMHJK/Unbenannte_Pr\u00e4sentation.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05S7URUA77","created_at":"2023-08-28T09:53","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"When a wind turbine and the incoming wind are not perfectly aligned one speaks of yaw misalignment.\r\nThis causes a reduction in power production and unwanted mechanical stresses on various turbine components.\r\n\r\nIn this case the turbine controller kicks in, checks the yaw misalignment, also called yaw error, and aligns the turbine with the wind in order to bring the yaw error back to zero.\r\n\r\nHowever, a turbine might still be misalignment with the wind, even though the monitored yaw error is zer...","hashtag":"","id":10,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"When a wind turbine and the incoming wind are not perfectly aligned one speaks of yaw misalignment.\r\nThis causes a reduction in power production and unwanted mechanical stresses on various turbine components.\r\n\r\nIn this case the turbine controller kicks in, checks the yaw misalignment, also called yaw error, and aligns the turbine with the wind in order to bring the yaw error back to zero.\r\n\r\nHowever, a turbine might still be misalignment with the wind, even though the monitored yaw error is zero.\r\nPotential reasons for this include malfunctioning sensors, issues with software, improper installation and others.\r\nHaving such a constant misalignment is called static yaw misalignment.\r\n\r\nThe goal of this challenge is to identify static yaw misalignment and mitigate its effects on the turbine.\r\nFor the challenge two open source datasets of two wind farms will be available\r\n\r\nChallenge Owner: **OST**\r\nFlorian Hammer","maintainer":"","name":"09: Identifying static yaw misalignment of wind turbines","phase":"Project","progress":5,"score":22,"source_url":"","stats":{"commits":0,"during":6,"people":4,"sizepitch":924,"sizetotal":924,"total":8,"updates":4},"summary":"","team":"anton_paris, david_skrob, florian_hammer, kirsten_tallner","team_count":4,"updated_at":"2023-09-19T08:52","url":"https://new-hack.energy.opendata.ch/project/10","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/TD1EYAB4S7SU7CL6PL491WNQ/challenge_9_yaw_misalignment_turbine.pptx.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05S7U60NN9","created_at":"2023-08-28T09:48","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"**Pronovo** is the entity that provides a very good overview of installed pv systems in Switzerland and many market players use the Pronovo database to **estimate PV production**.\r\nUnfortunately, there are major **delays** between the installation of the pv systems and the recording in the Pronovo database.\r\nThis wasn't a bigger issue so far, but the swiss pv market is **growing** rapidly and so the lag is causing a **general underestimation** of pv power production.\r\n\r\nWith **additional informa...","hashtag":"","id":1,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"**Pronovo** is the entity that provides a very good overview of installed pv systems in Switzerland and many market players use the Pronovo database to **estimate PV production**.\r\nUnfortunately, there are major **delays** between the installation of the pv systems and the recording in the Pronovo database.\r\nThis wasn't a bigger issue so far, but the swiss pv market is **growing** rapidly and so the lag is causing a **general underestimation** of pv power production.\r\n\r\nWith **additional information** on the pv systems such as installation date, audit date, date of entry into the database, we would like to identify patterns, trends and seasonalities in order to be able to better and continuously **estimate the current status of the pv expansion**.\r\n\r\nPitch:\r\n\ud83d\udcce [BFE_challenge.pdf](https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/public/1795/EQUT69RGDPTNN3ADRICZYXMT/BFE_challenge.pdf)","maintainer":"","name":"04: Catching up with photovoltaic expansion","phase":"Project","progress":5,"score":20,"source_url":"","stats":{"commits":0,"during":3,"people":2,"sizepitch":918,"sizetotal":1052,"total":5,"updates":3},"summary":"Published pv installed capacity underestimates pv production. We want to estimate pv installed capacity considering pv growth patterns","team":"jlgeering, adriana_marcucci","team_count":2,"updated_at":"2023-09-16T12:50","url":"https://new-hack.energy.opendata.ch/project/1","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/105/UISUADBCVXUYMSJ1EHQ8RT54/04_challenge_results.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05S47ENJ4E","created_at":"2023-08-28T09:52","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"A big problem especially for local governments today is identifying regions of cities where district heating could be applied. In the past they used simple methods based on building densities and sizes. Machine learning can be used to improve on this.\r\n\r\nThanks to our models we know a lot about cities and districts. We are looking for novel ways to use machine learning clustering methods to generate clusters of buildings that are close together and share important characteristics for connecting ...","hashtag":"","id":7,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"A big problem especially for local governments today is identifying regions of cities where district heating could be applied. In the past they used simple methods based on building densities and sizes. Machine learning can be used to improve on this.\r\n\r\nThanks to our models we know a lot about cities and districts. We are looking for novel ways to use machine learning clustering methods to generate clusters of buildings that are close together and share important characteristics for connecting to district heating.\r\n\r\nChallenge Owner: **Planeto Energy/University Geneva**\r\nJonathan Chambers, Stefano Cozza","maintainer":"","name":"07: Spatial Clustering for district energy","phase":"Project","progress":5,"score":20,"source_url":"","stats":{"commits":0,"during":4,"people":4,"sizepitch":611,"sizetotal":611,"total":6,"updates":2},"summary":"","team":"ari_jordan, simon_reichard, matteo_zoli, jon_chambers","team_count":4,"updated_at":"2023-09-19T08:52","url":"https://new-hack.energy.opendata.ch/project/7","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/3MTOUUGO4J787O8MW4C8EG1J/challenge7_clustering.pptx.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05SZRHCMQQ","created_at":"2023-08-28T09:45","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Based on the energy platform VGT, data & controllable devices for a (virtual) area/building are available (solar system, storage, charging station + e-car, heat pump).\r\n\r\nBy means of an artificial intelligence to be developed / implemented, the (virtual) building is to be modeled based on the existing data.\r\n\r\nBased on the weather forecasts, the (virtual) building shall be optimized either against the highest possible self-sufficiency level or against the lowest possible energy costs.\r\n\r\nDiffere...","hashtag":"","id":3,"ident":null,"image_url":"","is_challenge":true,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"Based on the energy platform VGT, data & controllable devices for a (virtual) area/building are available (solar system, storage, charging station + e-car, heat pump).\r\n\r\nBy means of an artificial intelligence to be developed / implemented, the (virtual) building is to be modeled based on the existing data.\r\n\r\nBased on the weather forecasts, the (virtual) building shall be optimized either against the highest possible self-sufficiency level or against the lowest possible energy costs.\r\n\r\nDifferent AIs can be implemented, since the VGT platform can be operated manufacturer-dependent.\r\n\r\nChallenge Owner: **AEW Energie AG und VGT AG**\r\n\r\n* Dominik Hanisch\r\n* Flavio M\u00fcller","maintainer":"","name":"02: Self-consumption optimization with artificial intelligence","phase":"Challenge","progress":0,"score":3,"source_url":"","stats":{"commits":0,"during":3,"people":3,"sizepitch":677,"sizetotal":677,"total":5,"updates":2},"summary":"","team":"josien_de_koning, tobias_graml, wolfram","team_count":3,"updated_at":"2023-09-19T08:43","url":"https://new-hack.energy.opendata.ch/project/3","webpage_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/3/3DS5L3OIJRDHSX6AYFF8BZOW/challenge_2_Selfconsumption_optimization_with_artificial_intelligence.pptx.pdf"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05S7VD7Y0M","created_at":"2023-09-04T05:53","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Today\u2019s energy supply is not up to the challenges of renewable, decentralized electricity production and the electrification of mobility and heat.\r\nIn a renewable energy supply, there is too much electricity when the sun is shining and too little when it is not. In addition, more and more electric cars and heat pumps will increase the demand for electricity and lead to more extreme load peaks. The electricity grid must become more flexible to react quickly to these fluctuations.\r\nElectric cars a...","hashtag":"","id":13,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"Today\u2019s energy supply is not up to the challenges of renewable, decentralized electricity production and the electrification of mobility and heat.\r\nIn a renewable energy supply, there is too much electricity when the sun is shining and too little when it is not. In addition, more and more electric cars and heat pumps will increase the demand for electricity and lead to more extreme load peaks. The electricity grid must become more flexible to react quickly to these fluctuations.\r\nElectric cars are an essential flexibility to achieve this goal. Today, many utilities have limited knowledge about the amount and timing of charging in their grid. Within this challenge, we would like to address this issue using data analytics.\r\nWe will develop an algorithm to determine whether an e-car is present at this measuring point. The charging power should be disaggregated from the household consumption. Within the framework of a project, we have made initial evaluations and found that massively more e-cars were detected than were known to the distribution network operator.\r\n\r\nFuture questions are:\r\nIs it possible to detect charging using the load curves measured at transformation stations?\r\nHow much better is the disaggregation if we use high-resolution data instead of 15-minute values?\r\n\r\nChallenge Owner: **aliunid AG und EKZ**\r\n","maintainer":"","name":"13: Disaggregation of electric charging from household consumption","phase":"Challenge","progress":0,"score":3,"source_url":"","stats":{"commits":0,"during":0,"people":1,"sizepitch":1335,"sizetotal":1335,"total":2,"updates":1},"summary":"","team":"michele_bolla","team_count":1,"updated_at":"2023-09-15T07:09","url":"https://new-hack.energy.opendata.ch/project/13","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05S7V49AFP","created_at":"2023-08-28T09:55","download_url":"https://docs.google.com/document/d/e/2PACX-1vSx3LA1q0BpiZqHtBUDUupnMVveNZAYk1XnuNEbeby15W8FE9lBPvwa092EyGHV10IUiVOwBMDaG4d-/pub","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"In this challenge, we provide you with a dataset containing consumption/production profiles.\r\nYour task is to develop a predictive model that can accurately forecast energy consumption/production patterns. The model needs to be deployed on a resource-constrained edge device, simulating real-world constraints. The targeted edge device is CLEMAP. For sake of simplicity, a virtual machine (VM) could be used to emulate the edge device with limited resources.\r\n\r\nTo make the deployment process simple,...","hashtag":"","id":8,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"In this challenge, we provide you with a dataset containing consumption/production profiles.\r\nYour task is to develop a predictive model that can accurately forecast energy consumption/production patterns. The model needs to be deployed on a resource-constrained edge device, simulating real-world constraints. The targeted edge device is CLEMAP. For sake of simplicity, a virtual machine (VM) could be used to emulate the edge device with limited resources.\r\n\r\nTo make the deployment process simple, we ask you to use NuvlaEdge technology. The main\r\ngoal is to deploy the developed forecasting module using NuvlaEdge, a powerful edge\r\ncomputing platform. The objective is to optimize and efficiently use resources while achieving\r\naccurate predictions.\r\n\r\nThe evaluation of your solution will be based on the following criteria:\r\n\r\nModel Performance: The accuracy of your predictive model will be assessed using the Mean Squared Error (MSE) score. We are looking for models that can effectively capture the energy consumption/production patterns and generate accurate forecasts.\r\n\r\nOverhead: Your solution must be deployed on a limited resources device. It must also \u201ccohabit\u201d with NuvlaEdge. Avoiding overload and efficiently using the available resources will be crucial. It is therefore paramount that the footprint (CPU and memory) of your solution is as low as possible.\r\n\r\nOptimisation: We encourage you to take into account optimization concerns. This includes strategies to minimize resource contention, reduce computational complexity, and optimize memory usage, all while maintaining accurate predictions.\r\n\r\nSummary steps for the challenge:\r\n\r\n1. Develop the forecasting module as a Docker container: Create a predictive model for energy consumption/production based on the provided dataset, and encapsulate it along with its dependencies into a Docker container.\r\n2. Deploy the forecasting container: Use the NuvlaEdge platform to deploy the developed forecasting module.\r\n3. Monitor/Optimise resource usage\r\n4. Evaluate model performance: Assess the performance of your deployed model using evaluation metrics such as the Mean Squared Error (MSE) score.\r\n\r\nBy participating in this challenge, you will gain valuable experience in addressing the resource constraints and optimization challenges inherent in edge computing environments. Your contributions will drive advancements in energy forecasting and pave the way for efficient deployment of predictive models on edge devices.\r\n\r\nChallenge Owner: **HES-SO**\r\n\r\n* Mohamad Moussa\r\n* Nabil Abdennadher\r\n* Philippe Glass","maintainer":"","name":"11: Predictive model for accurate production / consumption forecasts","phase":"Challenge","progress":0,"score":1,"source_url":"","stats":{"commits":0,"during":0,"people":1,"sizepitch":2583,"sizetotal":2583,"total":6,"updates":5},"summary":"","team":"philippe_glass","team_count":1,"updated_at":"2023-09-15T07:11","url":"https://new-hack.energy.opendata.ch/project/8","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C05SPFPQL2D","created_at":"2023-08-28T09:52","download_url":"","event_name":"Energy Data Hackdays 2023","event_url":"https://new-hack.energy.opendata.ch/event/1","excerpt":"Energy system scenarios are often visualized in Bar charts. For switzerland, a lot of distributed energy investments are necessary in the future. To get a deeper understanding where and under which circumstances these investments are optimal, the spacial component should be added to the visualization. The degrees of freedom increases a lot and therefore a lot of creativity is required.\r\n\r\nDevelop a Map of switzerland to show the energy transition of the electricity system on a spacial level.\r\n\r\n...","hashtag":"","id":9,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"Energy system scenarios are often visualized in Bar charts. For switzerland, a lot of distributed energy investments are necessary in the future. To get a deeper understanding where and under which circumstances these investments are optimal, the spacial component should be added to the visualization. The degrees of freedom increases a lot and therefore a lot of creativity is required.\r\n\r\nDevelop a Map of switzerland to show the energy transition of the electricity system on a spacial level.\r\n\r\nData will be provided from the Nexus-e model.\r\n\r\nPython/Matlab ArcGisPro\r\n\r\nChallenge Owner: **ESC/ ETH**\r\nSamuel Renggli","maintainer":"","name":"08: Spacial visualization of future energy scenarios","phase":"Challenge","progress":0,"score":0,"source_url":"","stats":{"commits":0,"during":1,"people":1,"sizepitch":621,"sizetotal":621,"total":2,"updates":1},"summary":"","team":"adriana_marcucci","team_count":1,"updated_at":"2023-09-15T07:45","url":"https://new-hack.energy.opendata.ch/project/9","webpage_url":""}],"name":"projects"}],"sources":[{"path":"https://new-hack.energy.opendata.ch/","title":"dribdat"}],"title":"Energy Data Hackdays 2023","version":"0.9.3"}