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\r\n\r\n## Data:\r\nWe will provide 100% synthetic \u2013 but realistic \u2013 consumption und production data for 2035 in transformer granularity for most of the transformers in our grid region. The data were generated in the scope of a Master Thesis in cooperation with the Power Systems Laboratory of ETH Zurich. The data is generated based on different assumptions and scenarios of the electricity production and consumption for the Canton of Zurich in 2035\r\n\r\n## Presentation \r\nhttps://youtu.be/gstFXCWe4rI?t=4776\r\n","maintainer":"","name":"15_Electricity \u201cHeartbeat\u201d 2035 in the Canton of Zurich","phase":"Project","progress":5,"score":37,"source_url":"","stats":{"commits":0,"during":4,"people":2,"sizepitch":1810,"sizetotal":1900,"total":4,"updates":2},"summary":"What do changes in the way and in the intensity electricity is consumed mean for our grid?","team":"benedikt_hilpisch, javad_hatami","team_count":2,"updated_at":"2021-10-26T13:20","url":"https://hack.energy.opendata.ch/project/83","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gf0203a066c_0_218"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"","created_at":"2021-09-24T14:42","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"#Starting Point\r\n\r\nSince the challenge 09 Customer Data Analytics was not accepted, we did together with another not accepted Challenge Owner a small very basic analytics. We tried to find correlations or patterns in the provided File.\r\n\r\n#Approach\r\n\r\nWe used the file provided (anonymized 4'000 data from customers) using BigML and tried to find correlations in the data. The most valuable attributes seemed to be \"Amount of contacts to customer center\", \"creditworthiness\" and \"Invoice-Type\". It tu...","hashtag":"","id":84,"ident":null,"image_url":"","is_challenge":false,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"#Starting Point\r\n\r\nSince the challenge 09 Customer Data Analytics was not accepted, we did together with another not accepted Challenge Owner a small very basic analytics. We tried to find correlations or patterns in the provided File.\r\n\r\n#Approach\r\n\r\nWe used the file provided (anonymized 4'000 data from customers) using BigML and tried to find correlations in the data. The most valuable attributes seemed to be \"Amount of contacts to customer center\", \"creditworthiness\" and \"Invoice-Type\". It turned out, that (unfortunately very obvious) people with high contact numbers to the customer center also have a bad creditworthiness (German: Bonity). \r\n\r\nSecondly we found a correlation, that the creditworthiness also correlates to the Invoice-Type. E-Bill-Customers pay their bill very fast (Bonity-Number is low whilst for customers with printed bills, it is much more likely to not pay the bills on time. \r\n\r\nOther correlations - like between the Amount of energy could not be found.\r\n\r\n#Conclusion\r\n\r\nAll in all, the dataset was not very valuable (since it had to be cut down for data protection reasons - we are not allowed to use it internally also). That was probably the reason, why the challenge was not accepted. Learning: We have to think about how we want to deal with DA in the future and how we can use it compliant to data protection.\r\n\r\n\r\n\r\n","maintainer":"nicola_ruch","name":"09.01 - Customer Data Analytics (Very Basic)","phase":"Project","progress":5,"score":33,"source_url":"","stats":{"commits":0,"during":18,"people":3,"sizepitch":1592,"sizetotal":1656,"total":18,"updates":14},"summary":"A smaller Project of the initial Idea 09 Customer Data Analytics","team":"nicola_ruch, benjamin_sawicki","team_count":3,"updated_at":"2021-09-24T17:27","url":"https://hack.energy.opendata.ch/project/84","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DZCE3325","created_at":"2021-08-19T19:01","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"# Why is it relevant?\r\n\r\nA spatio-temporal model is of great interest within the wind energy community for more accurate wind speed forecasting, which helps to better estimate power production and to develop optimized control strategies.\r\n\r\n# The challenge\r\n\r\nIn this challenge we want to develop a spatio-temporal model of an on-shore wind farm near a coast in Brazil.\r\nThe goal of the model is to be able to visualize and investigate the local winds and the interaction between turbines, in form of...","hashtag":"","id":85,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"# Why is it relevant?\r\n\r\nA spatio-temporal model is of great interest within the wind energy community for more accurate wind speed forecasting, which helps to better estimate power production and to develop optimized control strategies.\r\n\r\n# The challenge\r\n\r\nIn this challenge we want to develop a spatio-temporal model of an on-shore wind farm near a coast in Brazil.\r\nThe goal of the model is to be able to visualize and investigate the local winds and the interaction between turbines, in form of wake effects, within the farm.\r\n\r\n# Data\r\n\r\nFor this we provide a dataset that contains the 10-minute average wind speeds, the wind directions and the geographic coordinates of 32 wind turbines and 1 met mast tower for a period of 1 year.\r\n\r\nSee also: https://www.iet.hsr.ch/index.php?id=18317&L=562\r\n\r\n# Presentation \r\nhttps://youtu.be/gstFXCWe4rI?t=1680","maintainer":"","name":"06_Spatio-temporal model of an on-shore wind farm","phase":"Research","progress":10,"score":27,"source_url":"","stats":{"commits":0,"during":6,"people":3,"sizepitch":856,"sizetotal":917,"total":6,"updates":3},"summary":"Better market predictions before you put energy into the grid","team":"regis_andreoli, florian_hammer, maria_giulia_ratti","team_count":3,"updated_at":"2021-10-26T13:14","url":"https://hack.energy.opendata.ch/project/85","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gede28e4fe7_45_66"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DT0T3284","created_at":"2021-07-15T15:44","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"# Tasks\r\n- Get yourself familiar with the TRE market in Switzerland \r\nAnalysis of historical data (TRE and TERRE bidding prices and volumes, TRE and TERRE activations) to derive patterns of seasonality in the data\r\n- Analysis of the TRE market before and after TERRE introduction in October 2020\r\n- Focus on the selected factors (such as intraday prices) and identify the influence on the TRE energy prices\r\n\r\n**Note: signing a legal agreement with Swissgrid is required to access some of this data.*...","hashtag":"","id":86,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"# Tasks\r\n- Get yourself familiar with the TRE market in Switzerland \r\nAnalysis of historical data (TRE and TERRE bidding prices and volumes, TRE and TERRE activations) to derive patterns of seasonality in the data\r\n- Analysis of the TRE market before and after TERRE introduction in October 2020\r\n- Focus on the selected factors (such as intraday prices) and identify the influence on the TRE energy prices\r\n\r\n**Note: signing a legal agreement with Swissgrid is required to access some of this data.**\r\n\r\n# Presentation\r\nhttps://youtu.be/gstFXCWe4rI?t=3118","maintainer":"","name":"10_Trends in tertiary energy prices","phase":"Project","progress":5,"score":23,"source_url":"","stats":{"commits":0,"during":4,"people":2,"sizepitch":556,"sizetotal":588,"total":5,"updates":2},"summary":"Trends in tertiary energy prices","team":"emma_romei, gianluca_mancini","team_count":2,"updated_at":"2021-10-26T13:16","url":"https://hack.energy.opendata.ch/project/86","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gcb24bd584d_3_10"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DLPN94SF","created_at":"2021-08-19T19:37","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"## Digital twins for domestic hot water and heating distribution systems\r\n\r\n### What is it about?\r\nIn Switzerland, heating and cooling is responsible for 80 % of the residential energy demand. To keep\r\nappartements cosily warm and to provide always abundantly cold/hot water at any time. However,\r\ncomplex energy and water systems are required to provide this service 24/7. Motivated by the IOT\r\ntrend, also the number of sensors installed and recording data from residential energy systems is\r\nstead...","hashtag":"","id":87,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"## Digital twins for domestic hot water and heating distribution systems\r\n\r\n### What is it about?\r\nIn Switzerland, heating and cooling is responsible for 80 % of the residential energy demand. To keep\r\nappartements cosily warm and to provide always abundantly cold/hot water at any time. However,\r\ncomplex energy and water systems are required to provide this service 24/7. Motivated by the IOT\r\ntrend, also the number of sensors installed and recording data from residential energy systems is\r\nsteadily increasing.\r\nTo operate these systems energy efficiently, clever control strategies are required to prevent\r\noverprovision of hot water or overheating of the rooms. In particular, digital twins attracted recently\r\nmuch attention because these (data-driven) system models enable the optimisation of the energy\r\nsystem without touching it. Join this challenge to explore new applications of data from your home\r\nor to actually build digital twins for hot water provision systems or appartements.\r\n\r\n## The challenge: \r\nIn this challenge you will have the possibility to explore the multiple benefits of digital\r\ntwins. Depending on your background three different directions of the challenge are waiting on you:\r\n\r\n1. Hot water provision: Based on measurement data of a domestic hot water system build a\r\ndigital twin of the system and explore how well you can predict the hot water demand of the\r\nsystem?\r\n\r\n2. Appartement: Start from monitoring data of an apartment and develop a data-driven digital\r\ntwin to predict its behaviour based on external parameters such as the solar radiation.\r\n\r\n3. Ecosystem: Which benefits can digital twin provide in the residential context? Which new\r\napplications could be realised? Who owns a digital twin? Which fears could arise?\r\n\r\nIndependent of whether you are an energy expert, a data scientist or an enthusiast, this challenge\r\noffers you the possibility to contribute to the energy turnaround in your home.\r\n\r\n### Data: \r\nTwo disparate datasets are ready for this challenge:\r\n\r\n1. Monitoring data from two identical domestic hot water preparation system in different\r\nbuildings. This data set is ideally suited if you would like to develop a digital twin yourself or\r\nto showcase the prediction of the hot-water consumption of different users.\r\n\r\n2. Monitoring data from a flat including time-resolved room temperature measurements, heat\r\nfluxes to the rooms and solar radiation data.\r\n\r\n### Presentation \r\nhttps://youtu.be/gstFXCWe4rI?t=2648","maintainer":"","name":"08_Digital twins for domestic heating systems","phase":"Project","progress":5,"score":22,"source_url":"","stats":{"commits":0,"during":2,"people":1,"sizepitch":2489,"sizetotal":2626,"total":2,"updates":1},"summary":"How can digital twins help us to safe energy while keeping our appartements warm and guaranteeing that sufficient hot water is available?","team":"wolfram","team_count":1,"updated_at":"2021-10-26T13:15","url":"https://hack.energy.opendata.ch/project/87","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gf01f701ab1_0_29"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DHPQ95HT","created_at":"2021-08-19T19:41","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"##Tasks\r\n* Analysis of historical data (frequency deviation measurements for FCR, control signal sent by Swissgrid for aFRR) to derive patterns in the data (e.g., using Machine Learning techniques, such as clustering)\r\n* Identify the most prominent patterns in the data and collect them into a set of representative signal extracts\r\n* Combine the signal extracts into an overall signal that is representative enough of various scenarios, for example, base-case signal, worst-case signal with regards ...","hashtag":"","id":88,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"##Tasks\r\n* Analysis of historical data (frequency deviation measurements for FCR, control signal sent by Swissgrid for aFRR) to derive patterns in the data (e.g., using Machine Learning techniques, such as clustering)\r\n* Identify the most prominent patterns in the data and collect them into a set of representative signal extracts\r\n* Combine the signal extracts into an overall signal that is representative enough of various scenarios, for example, base-case signal, worst-case signal with regards to power capacity, worst-case signal with regards to energy capacity, worst-case signal with regards to ramping power, etc.\r\n* Determine the list of tests to be included in the new prequalification test, making use of the signal extracts among other things\r\n\r\n**Note: signing a legal agreement with Swissgrid is required to access some of this data.**","maintainer":"","name":"11_Pre Qualification Test","phase":"Project","progress":5,"score":21,"source_url":"","stats":{"commits":0,"during":2,"people":0,"sizepitch":851,"sizetotal":954,"total":2,"updates":2},"summary":"deriving a representive frequency profile, with which TSO can certfy the providers for the FCR service. ","team":"","team_count":0,"updated_at":"2021-09-25T15:05","url":"https://hack.energy.opendata.ch/project/88","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gf0203a066c_0_90"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02EACN83T2","created_at":"2021-09-21T12:33","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"# Can you beat the model performance benchmark? \r\n\r\nVIA seeks models to help address the most important clean energy challenges:\r\n\r\n- 24-hour load prediction\r\n- Anomaly detection\r\n- NILM (non-intrusive load monitoring)\r\n\r\n## The challenge:\r\nThree smart meter datasets are available:\r\n\r\n- Low Carbon London dataset with 167 million rows (link)\r\n- Reference Energy Disaggregation Dataset with 1.7 million rows (link)\r\n- REFIT Electrical Load Measurements with 114 million rows (link)\r\n\r\nDo you have a n...","hashtag":"","id":89,"ident":null,"image_url":"","is_challenge":false,"is_webembed":true,"logo_color":"","logo_icon":"","longtext":"# Can you beat the model performance benchmark? \r\n\r\nVIA seeks models to help address the most important clean energy challenges:\r\n\r\n- 24-hour load prediction\r\n- Anomaly detection\r\n- NILM (non-intrusive load monitoring)\r\n\r\n## The challenge:\r\nThree smart meter datasets are available:\r\n\r\n- Low Carbon London dataset with 167 million rows (link)\r\n- Reference Energy Disaggregation Dataset with 1.7 million rows (link)\r\n- REFIT Electrical Load Measurements with 114 million rows (link)\r\n\r\nDo you have a new idea for a model using one of the datasets? Can you improve on the existing models? \r\n\r\nUtilities will be able to \u201cupvote\u201d and \u201cdownvote\u201d a description of your proposed model to give you feedback before you begin model development. Interested utilities will reach out to prefered analysts through the secure VIA Insights Market\u2122 (logins provided at the event) and be able to directly test your model on their data. Each utility\u2019s data remains private and secure. You will receive a ranking of the performance of your model versus industry benchmarks.\r\n\r\n## Requirements:\r\n\r\nModels must be in Tensorflow 2.0 protocol buffer (.pb) format and a link to your model must be submitted as a comment in your model proposal page on VIA Insights Market\u2122.\r\n\r\n# Presentation \r\nhttps://youtu.be/gstFXCWe4rI?t=2176\r\n\r\n## About VIA:\r\n\r\nVIA is powering the clean energy revolution. VIA's AI platform helps organizations drive operational efficiencies and collaborate across the energy ecosystem by overcoming challenges related to data quality, data scarcity, and data privacy. We work with governments and the largest power providers in the US, Europe, and Asia. Now, we need your help to make an even bigger difference.\r\n","maintainer":"","name":"07_Can you beat the model performance benchmark? ","phase":"Project","progress":5,"score":18,"source_url":"","stats":{"commits":0,"during":2,"people":1,"sizepitch":1708,"sizetotal":1809,"total":2,"updates":1},"summary":"Do you have a new idea for a model using one of the datasets? Can you improve on the existing models? ","team":"benbowler","team_count":1,"updated_at":"2021-10-26T13:15","url":"https://hack.energy.opendata.ch/project/89","webpage_url":"https://docs.google.com/presentation/d/e/2PACX-1vRBqDBoQqQdZXbrKNXzYYQZAGX4ikVOXibj4sGLcfpFzwY8p5vaUYbn8Nrs3vJvig/embed?start=false&loop=false&delayms=3000&slide=id.gf2cd4fb721_29_155"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DHPE5HJR","created_at":"2021-08-19T19:39","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"Identification of patterns and correlations of anonymised electricity customer data for the development and optimisation of products and services, processes and customer-utility communication.\r\n\r\n## The Challenge:\r\nThe aim is to develop an approach that complies with data protection requirements and allows Swiss utility companies to a) correctly classify their customers, taking into account various data sources and influencing factors, and b) develop products and services tailored to their needs...","hashtag":"","id":90,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"Identification of patterns and correlations of anonymised electricity customer data for the development and optimisation of products and services, processes and customer-utility communication.\r\n\r\n## The Challenge:\r\nThe aim is to develop an approach that complies with data protection requirements and allows Swiss utility companies to a) correctly classify their customers, taking into account various data sources and influencing factors, and b) develop products and services tailored to their needs. Furthermore, the application should contribute to improving internal processes.\r\n\r\n## Data:\r\nanonymised master data\r\n","maintainer":"","name":"09_Customer data analysis","phase":"Challenge","progress":-100,"score":1,"source_url":"","stats":{"commits":0,"during":6,"people":2,"sizepitch":617,"sizetotal":617,"total":6,"updates":4},"summary":"","team":"nicola_ruch","team_count":2,"updated_at":"2021-09-24T15:24","url":"https://hack.energy.opendata.ch/project/90","webpage_url":"https://hack.opendata.ch/project/787"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02EABYA9G8","created_at":"2021-08-19T18:17","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"#\u201cCybersecurity Navigator for Building IoT\u201d\r\n\r\nDigitalisation can make a relevant and positive contribution to climate and energy targets in the building sector. To achieve this, the interaction of the individual trades, disciplines and technologies in buildings must be improved. IoT systems are a key technology for achieving the full potential of energy efficiency in buildings. A current hurdle is the low transparency and acceptance of the market towards IoT systems. \r\nWe assume that transparen...","hashtag":"","id":91,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"#\u201cCybersecurity Navigator for Building IoT\u201d\r\n\r\nDigitalisation can make a relevant and positive contribution to climate and energy targets in the building sector. To achieve this, the interaction of the individual trades, disciplines and technologies in buildings must be improved. IoT systems are a key technology for achieving the full potential of energy efficiency in buildings. A current hurdle is the low transparency and acceptance of the market towards IoT systems. \r\nWe assume that transparency in the area of data protection and cyber security can increase acceptance in the market for IoT systems and that this will have a positive impact on energy efficiency in buildings.\r\nA preliminary project exists in which students of the HSLU analyse and categorise more than 80 guidelines, standards and guidance documents in the area of data protection and cybersecurity. \r\n\r\n##Challenge:\r\nWe create a cyber security tool! But it goes even further: with a public information security tool, we can provide transparency in the area of cybersecurity and data protection to all interested actors in the building market (investors, planners, manufacturers, managers and tenants), and thus support digitalisation in the building sector.\r\nBy means of a user-friendly interface, users of the tool could get a list of documents relevant to them based on a selection of criteria.\r\n\r\n##Data:\r\nCollection of relevant documents in the field of data protection and cybersecurity\r\n\r\n(1) The preliminary project was developed within the framework of the \"Connectivity in buildings\" project launched by energie-cluster.ch. The aim of the project is to identify the current challenges of connectivity in buildings (KiG) and to develop possible solutions for optimisation. Demands and expectations regarding the interaction of technical systems with each other, data protection and cyber security as well as the acceptance of users and the interaction between technical systems and users will be discussed jointly with the involvement of all stakeholders.\r\n","maintainer":"","name":"02_Cybersecurity Navigator for Building IoT","phase":"Challenge","progress":-100,"score":0,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":2039,"sizetotal":2153,"total":0,"updates":0},"summary":"A tool to informing about IoT cybersecurity and data protection, to support digitalisation in the building sector.","team":"","team_count":0,"updated_at":"2021-09-24T08:03","url":"https://hack.energy.opendata.ch/project/91","webpage_url":"https://old.energie-cluster.ch/de/wissenstransfer/innovationsgruppen-(ig)/konnektivitaet-im-gebaeude-(ig-kig)/projekt-konnektivitaet-im-gebaeude-(kig)-6059.html"},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DPGGTFT8","created_at":"2021-08-19T19:51","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"## The Challenge\r\nBy clicking on their own house and selecting their own electricity mix, consumers should be able to understand topographically where their electricity comes from. Which is the nearest distribution box? Where do other lines branch off? At which transformer station does \"my\" electricity pass? And which substation is responsible for my supply? \r\n \r\nIt should be possible to trace back topographically correctly to the boundary of the electricity supplier. Standard network states are...","hashtag":"","id":92,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"## The Challenge\r\nBy clicking on their own house and selecting their own electricity mix, consumers should be able to understand topographically where their electricity comes from. Which is the nearest distribution box? Where do other lines branch off? At which transformer station does \"my\" electricity pass? And which substation is responsible for my supply? \r\n \r\nIt should be possible to trace back topographically correctly to the boundary of the electricity supplier. Standard network states are used for this. At the physical boundary of the plant, the selected electricity mix is represented schematically and visually: Hydropower (river power plant, storage power plant), nuclear power plant, coal power, solar, wind, ...\r\n","maintainer":"","name":"13_Where does my electricity come from?","phase":"Challenge","progress":-100,"score":0,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":729,"sizetotal":791,"total":0,"updates":0},"summary":"The path of electricity from the end consumer's point of view.","team":"","team_count":0,"updated_at":"2021-09-24T08:27","url":"https://hack.energy.opendata.ch/project/92","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DHNLDXHT","created_at":"2021-07-15T16:52","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"##What is it about?\r\nIn order to achieve the goals for the swiss energy strategy we must increase sustainable energy production and consumption.\r\nThere is already information available for the current status of the expansion of PV, renewable heating and e-mobility. The web tool \"Energiereporter\" (https://www.energieschweiz.ch/tools/energiereporter/) shows the sustainable development at municipality level.\r\nVisitors can look up on the website the development in their municipality in the areas of ...","hashtag":"","id":93,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"##What is it about?\r\nIn order to achieve the goals for the swiss energy strategy we must increase sustainable energy production and consumption.\r\nThere is already information available for the current status of the expansion of PV, renewable heating and e-mobility. The web tool \"Energiereporter\" (https://www.energieschweiz.ch/tools/energiereporter/) shows the sustainable development at municipality level.\r\nVisitors can look up on the website the development in their municipality in the areas of electric mobility, solar power and renewable heat.\r\nWe want to find out which are the key influencers of these sustainable indicators at municipality level. \r\nIt would be a great added value for the municipalities to know which are the most important influencing factors for their sustainable development.\r\n\r\n##Challenge:\r\nWith the complete data set of the Energy Reporter including the sustainability KPI's and other (e.g.socio-demographic, weather, topographic, infrastructure, political and electricity-costs) data on municipality level, the participants could identify the most important influencing factors leading to sustainability. A small machine learning project would be suitable for this analysis. Participants are free to choose the algorithm or statistical method.\r\n\r\n##Data:\r\ncsv with all municipalities and indicators of the energy reporter.\r\nCsv with all municipalities, attributes on population demographics and electricity prices.\r\nIn addition, a wide range of municipal data is publicly available. We encourage participants to use opendata.swiss to find interesting data at the municipality level that can explain sustainable development.\r\n\r\n\r\nThis project is a follow up of the challenge proposed at the Energy & Climate Hack. Accordingly, the precise frame of the challenge might be influenced through the results of the teams.\r\n","maintainer":"","name":"03_Local Sustainability booster","phase":"Challenge","progress":-100,"score":0,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":1848,"sizetotal":1966,"total":0,"updates":0},"summary":"\u200b\u200b Which features boost local sustainability? - Impulses and hurdles for sustainable development on municipality level","team":"","team_count":0,"updated_at":"2021-09-24T08:11","url":"https://hack.energy.opendata.ch/project/93","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DPGKBKJA","created_at":"2021-09-03T12:58","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"## What is it about?\r\nIn 2018, the University of St.Gallen, in collaboration with Swisspower Innovation and other practical partners, identified the megatrends relevant to the energy industry on the basis of various workshops.\r\n\r\n## The Challenge:\r\nAs part of the Challenge, the trends already found will be verified or new trends identified with the help of modern AI- and statistics-based information and text analysis methods. \r\n\r\n## Data:\r\nFor this purpose, programs and presentations of selected...","hashtag":"","id":94,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"## What is it about?\r\nIn 2018, the University of St.Gallen, in collaboration with Swisspower Innovation and other practical partners, identified the megatrends relevant to the energy industry on the basis of various workshops.\r\n\r\n## The Challenge:\r\nAs part of the Challenge, the trends already found will be verified or new trends identified with the help of modern AI- and statistics-based information and text analysis methods. \r\n\r\n## Data:\r\nFor this purpose, programs and presentations of selected conferences from the energy sector of the past 5 years will be made available.\r\nOther relevant, publicly available data sources could be job boards (jobs.ch), startup monitors (Swiss Environment and Energy Innovation Monitor) and annual reports of energy utilities.\r\n","maintainer":"","name":"14_ Trend scouting for the energy industry","phase":"Challenge","progress":-100,"score":0,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":766,"sizetotal":894,"total":0,"updates":0},"summary":"Identifying trends in the energy industry with the help of modern AI- and statistics-based information and text analysis methods","team":"","team_count":0,"updated_at":"2021-09-24T08:33","url":"https://hack.energy.opendata.ch/project/94","webpage_url":""},{"autotext":"","autotext_url":"","category_id":"","category_name":"","contact_url":"https://openenergydata.slack.com/archives/C02DLNTRBFD","created_at":"2021-07-15T15:42","download_url":"","event_name":"Energy Data Hackdays 2021","event_url":"https://hack.energy.opendata.ch/event/6","excerpt":"# Generation of short-term (sub-second) probabilistic forecasting of photovoltaic\r\n## What is it about?\r\nThe goal of the challenge is to generate short-term (sub-second) probabilistic forecasting of photovoltaic (PV) generation and load consumption when only their aggregated prosumption is being monitored. The challenge would involve the disaggregation of the monitored prosumption into PV generation and load consumption in real-time. \r\n## Data\r\nThe available dataset includes high-resolution hist...","hashtag":"","id":95,"ident":null,"image_url":"","is_challenge":true,"is_webembed":false,"logo_color":"","logo_icon":"","longtext":"# Generation of short-term (sub-second) probabilistic forecasting of photovoltaic\r\n## What is it about?\r\nThe goal of the challenge is to generate short-term (sub-second) probabilistic forecasting of photovoltaic (PV) generation and load consumption when only their aggregated prosumption is being monitored. The challenge would involve the disaggregation of the monitored prosumption into PV generation and load consumption in real-time. \r\n## Data\r\nThe available dataset includes high-resolution historical measurements (up to 100ms) of the nodal prosumption and the global horizontal irradiance (GHI) from a MW-scale distribution grid at Aigle.\r\n\r\n@Distributed Electrical Systems Laboratory DESL / EPFL\r\n\r\n\r\n","maintainer":"","name":"01_Short-term Photovoltaic Forecasting","phase":"Challenge","progress":-100,"score":0,"source_url":"","stats":{"commits":0,"during":0,"people":0,"sizepitch":703,"sizetotal":703,"total":0,"updates":0},"summary":"","team":"","team_count":0,"updated_at":"2021-09-24T08:03","url":"https://hack.energy.opendata.ch/project/95","webpage_url":"https://www.epfl.ch/labs/desl-pwrs/research/forecasting/"}],"name":"projects"}],"sources":[{"path":"https://dribdat.cc","title":"dribdat"}],"title":"Energy Data Hackdays 2021","version":"0.2.0"}