{"contributors":[],"created":"2026-09-07T19:44","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\nHackathons bring together energy experts, analysts, data scientists, programmers, students and interested people from all disciplines to work together with team spirit and enthusiasm on solutions to concrete problems. Take part! We are happy to announce that after the good experiences and results of the Hackdays since 2019, the next edition of the Energy Data Hackdays will take place in Brugg-Windisch with existing and new partners - committed to tradition.\r\n\r\n**The event moderation and the presentations take place in English**\r\n\r\nFurther Information\r\nInfos auf Deutsch\r\nInformations en fran\u00e7ais","homepage":"https://energydatahackdays.ch/","keywords":["EnergyHackdays","EDHD2025"],"licenses":[{"name":"ODC-PDDL-1.0","path":"http://opendatacommons.org/licenses/pddl/","title":"Open Data Commons Public Domain Dedication & License 1.0"}],"name":"event-9","resources":[{"data":[{"aftersubmit":"","boilerplate":"","certificate_path":"","community_embed":"","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\nHackathons bring together energy experts, analysts, data scientists, programmers, students and interested people from all disciplines to work together with team spirit and enthusiasm on solutions to concrete problems. Take part! We are happy to announce that after the good experiences and results of the Hackdays since 2019, the next edition of the Energy Data Hackdays will take place in Brugg-Windisch with existing and new partners - committed to tradition.\r\n\r\n**The event moderation and the presentations take place in English**\r\n\r\nFurther Information\r\nInfos auf Deutsch\r\nInformations en fran\u00e7ais","ends_at":"2025-09-12T15:00","gallery_url":"https://www.energydatahackdays.ch/images/Archiv/2021/_800x400_crop_center-center_90_none/2606/EDHD_2021_Tag1__57.webp","has_finished":true,"has_started":false,"hashtags":"#EnergyHackdays #EDHD2025","hostname":"Fachhochschule Nordwestschweiz","id":9,"instruction":"","location":"Campus Brugg-Windisch","location_lat":47.48166,"location_lon":8.21134,"logo_url":"https://bucketeer-036aa605-c047-4623-8610-f1764b90cf98.s3.amazonaws.com/openenergydata/1/7T74AQ95UG539MTCT5DK8KQH/edh24bulb.png","name":"Energy Data Hackdays","starts_at":"2025-09-11T07:00","summary":"","webpage_url":"https://energydatahackdays.ch/"}],"name":"events"},{"data":[{"autotext":"# 10 - Watts and Wallets - Visualizing Energy and Money Flows in Switzerland-s Power Market\n\n> All code produced during the Energy Data Hackdays or other Events and Programs organized,\n> co-organized or hosted by EDIH and submitted for review shall be shared on [EDIH\u00e2\u20ac\u2122s Gitlab repository](https://gitlab.com/edhd/).\n>\n> EDIH expects full documentation including:\n>\n> 1) Description of all data used (**not** the data itself)\n> 2) Output data description (**not** the data itself) and where they are stored\n> 3) Explanation of features used\n> 4) A requirements file with all packages and versions used\n> 5) Environment code to be run\n\n\n\n## Challenge\n\n*A brief description of your challenge.*\n\n## Getting Started\n\n*These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.*\n\n### Prerequisites\n\n*What things you need to install the software and how to install them.*\n\n### Installation\n\n*A step by step series of examples that tell you how to get a development env running.*\n\n## Data\n\n## Input data\n\n*A brief description of the input data. Try to use relative paths to the data and define any environment variables.*\n\n## Output Data\n\n*A brief description of the output data. Provide information about the features and the location of the data.*\n\n## Contributing\n\nPlease read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct and the process for submitting merge requests.\n\n## Group Members\n\n- First Name (role)\n- First Name (role)\n- First Name (role)\n\n## License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE.md) file for details.\n","autotext_url":"https://gitlab.com/edhd/2025/watts-wallets-visualizing-energy-money-flows-in-switzerlands-power-market/team1","category_id":"","category_name":"","contact_url":"https://gitlab.com/edhd/2025/watts-wallets-visualizing-energy-money-flows-in-switzerlands-power-market/team1/issues","created_at":"2025-10-17T13:03","download_url":"","event_name":"Energy Data Hackdays","event_url":"https://new-hack.energy.opendata.ch/event/9","excerpt":"# 10 - Watts and Wallets - Visualizing Energy and Money Flows in Switzerland-s Power Market\n\n> All code produced during the Energy Data Hackdays or other Events and Programs organized,\n> co-organized or hosted by EDIH and submitted for review shall be shared on [EDIH\u00e2\u20ac\u2122s Gitlab repository](https://gitlab.com/edhd/).\n>\n> EDIH expects full documentation including:\n>\n> 1) Description of all data used (**not** the data itself)\n> 2) Output data description (**not** the data itself) and where they are...","hashtag":"","id":131,"ident":"","image_url":"https://gitlab.com/uploads/-/system/project/avatar/74157383/label1.png","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"","maintainer":"","name":"10 - Watts and Wallets - Visualizing Energy and Money Flows in Switzerland-s Pow","phase":"Challenge","progress":0,"score":0,"source_url":"https://gitlab.com/edhd/2025/watts-wallets-visualizing-energy-money-flows-in-switzerlands-power-market/team1","stats":{"commits":0,"during":0,"people":0,"sizepitch":0,"sizetotal":1657,"total":1,"updates":1},"summary":"","team":"","team_count":0,"updated_at":"2025-10-17T13:03","url":"https://new-hack.energy.opendata.ch/project/131","webpage_url":""},{"autotext":"# 1 - Modeling PV Production with Machine Learning\r\n\r\n## Challenge\r\n\r\nFor small photovoltaic systems (<30kWp), CKW currently only measures the energy fed into its grid. This creates a gap in understanding the actual energy production and self-consumption of these systems. \r\nThe objective of this challenge is to bridge that gap by developing a machine learning model that can accurately estimate total energy production and self-consumption. \r\n\r\nDevelop a machine learning model to estimate the energy production of small photovoltaic (PV) systems (<30kWp) using feed-in data, weather information, and egid data.\r\nThe goal is to enable innovative and intelligent grid- and market-oriented solar energy solutions.\r\n\r\n### Why it matters for CKW\r\nThis challenge has significant real-world implications: \r\n- Starting next year, PV production will be limited to **70% of the panel\u2019s peak output** as per new regulations. \r\n- Accurate production estimates will help: \r\n - Quantify energy losses due to these limits. \r\n - Support the creation of innovative, market-oriented solar energy products. \r\n - Enhance CKW\u2019s ability to make solar energy more attractive and efficient for customers. \r\n\r\n\r\n### Prerequisites\r\n\r\n- Renku or Local IDE like VSCode or Pycharm\r\n- [Python 3.10+](https://www.python.org/)\r\n- [Git](https://git-scm.com/)\r\n- [pip](https://pip.pypa.io/en/stable/)\r\n\r\n## Getting Started\r\n\r\n1. Clone repository\r\n```bash\r\n git clone https://gitlab.com/edhd/2025/modeling-pv-production-with-machine-learning/team1.git\r\n```\r\n\r\n2. Install requirements\r\n```python\r\n pip install -r requirements.txt\r\n```\r\n\r\n## Data\r\n\r\n- CKW\u2019s grid includes approximately **9,600 PV systems** smaller than 30kWp without production measurement. \r\n- Participants will work with: \r\n - **Time series energy data** \r\n - **Weather data** (provided by Meteomatics) \r\n - **EGID data** \r\n- The dataset is preprocessed with basic feature engineering, allowing participants to focus on building the best possible model. \r\n- Both traditional machine learning approaches and advanced neural networks, such as **LSTMs**, are encouraged. \r\n\r\n## Input data\r\n\r\nThe data is found on a azure blob storage and can be accessed directly in renku or with a connection string provided by the challenge owner at the Hackday. \r\n\r\n\r\n## Results and machine learning models\r\n\r\nThe results and created machine learning models can be found in the `models` folder \r\n\r\n## Group Members\r\n\r\n- Enrique Romano (Challenge owner)\r\n- First Name (role)\r\n- First Name (role)\r\n\r\n## License\r\n\r\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE.md) file for details.\r\n","autotext_url":"https://gitlab.com/edhd/2025/modeling-pv-production-with-machine-learning/team1","category_id":"","category_name":"","contact_url":"https://gitlab.com/edhd/2025/modeling-pv-production-with-machine-learning/team1/issues","created_at":"2025-10-17T12:51","download_url":"","event_name":"Energy Data Hackdays","event_url":"https://new-hack.energy.opendata.ch/event/9","excerpt":"# 1 - Modeling PV Production with Machine Learning\r\n\r\n## Challenge\r\n\r\nFor small photovoltaic systems (<30kWp), CKW currently only measures the energy fed into its grid. This creates a gap in understanding the actual energy production and self-consumption of these systems. \r\nThe objective of this challenge is to bridge that gap by developing a machine learning model that can accurately estimate total energy production and self-consumption. \r\n\r\nDevelop a machine learning model to estimate the ene...","hashtag":"","id":130,"ident":"1","image_url":"https://gitlab.com/uploads/-/system/project/avatar/74157124/label1.png","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"","maintainer":"","name":"Modeling PV Production with Machine Learning","phase":"Challenge","progress":0,"score":0,"source_url":"https://gitlab.com/edhd/2025/modeling-pv-production-with-machine-learning/team1","stats":{"commits":0,"during":0,"people":0,"sizepitch":0,"sizetotal":2710,"total":1,"updates":1},"summary":"","team":"","team_count":0,"updated_at":"2025-10-17T13:03","url":"https://new-hack.energy.opendata.ch/project/130","webpage_url":""}],"name":"projects"}],"sources":[{"path":"https://new-hack.energy.opendata.ch/","title":"dribdat"}],"title":"Energy Data Hackdays","version":"0.9.3"}