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Smart Cities on the Rise

- October 13, 2020 in Uncategorized

Cities take their innovation into their own hands!  Three hackathons in a row, tackling different aspects of urban life at different cities scales, show us how smart cities will be developed locally, by and for their inhabitants, with Open Data as raw material for their reflection and solutions. The first hackathon, Smart City lab Lenzburg, will take place either in Lenzburg or online on the 14th and 15th of November 2020 and is strongly anchored in the local community: taking place centrally in the beautiful Aula of one of the schools, it will tackle many questions that are specific to Lenzburg: for example after a new 2000W neighbourhood was build, the following questions arose: How to optimize this new way of urban living? Which lessons can we learn? Which solutions could be adopted by the rest of the city? Another challenge will be how to reunite the different parts of Lenzburg, which were cut up by the train tracks and the highway at different moments of its development? Could optimizing public transport to the different neighborhoods be part of the solution? Or making initiatives and activities in different parts of the city more visible to the rest of Lenzburg? Questions also arise from the side of the city administration and services, and from the energy provider. Solutions might be digital or not, but while their starting point is local, the solutions might concern us all. Shortly after, on the 27th and 28th of November the Shape my City – Open Data Hackdays, organized by the students of the master’s degree course in Applied Information and Data Science of the Lucerne University of Applied Science and Arts, will take place in the Laboratorium, a start-up incubator hosting the event for the second year in Lucerne. For this larger touristic and academic city, one of the focuses will be to understand the needs of the different types of inhabitants, users, visitors and stakeholders of the city. How can we shape the interaction for better communication and services? And how can we incentivise behavioural changes for a more harmonious and sustainable city life? The other topics revolve around exploring the potential of the urban fabric for energy production, climate change mitigation and here too, the model role of 2000W neighborhoods. Then, on the 29th & 30th April 2021, the Energy and Climate Hacks will take place in the city of Bern, as a parallel event to the Decarbonizing Cities Conference. During this event, smart urban living will be addressed through the lens of energy efficiency not only in Bern but also in some british cities, throwing a bridge between common issues and needs across borders. The beauty of this series of events is not only that it will allow us to go deeper in the development of solutions, but also that it exemplifies how bottom-up locally embedded initiatives actually tackle global issues and how those proposals, tested at a local scale, might help our society beyond their original purpose. These events are open and free of charge, like all our other hackdays, so take part, you too have a take on how our future smart cities should be!

Energy Data Hackdays 2020, the results!

- October 6, 2020 in 2020, Brugg, Daten, Energie, Energy, event, Forschung, hackathon, Hackdays, machine learning, Optimization

The excitement of the new edition of the Energy Hackdays in Brugg was a bit special this year. Besides the usual sweet little heart pinch of the leap into a new group, the discovery of the challenges and the satisfaction of seeing this particular event repeating for the second time in Brugg, there was happiness but also respect about having the Energy Hackdays taking place mostly on site at the Hightech Zentrum Aarau. So we met in person and as far as we can say, it has been worth it! 13 really ambitious and technical challenges met 85 participants who were nonetheless ambitious and highly qualified! Two big themes emerged this year and predictions based on machine learning was one of them. Predicting performance, usage patterns, anomalies or even failure, in order to plan, use and maintain infrastructure more accurately.  Reaching these goals of course allows a much better resource and production management. The other big topic covered by several challenges was the question of visualization and interfaces, especially for smart-meters: How to help users, scientists, producers or end-consumers to read flows of data and allow them to interpret and decide or react appropriately to a given data supported information? How can they analyse and control different aspects of their infrastructure or installation? Tangent to this topic were challenges that attempted to allow a market overview for the consumer, in this case the market of E-Car charging stations, or to visualize the overall live electricity consumption of Switzerland. As far as I can judge and from what I heard from the challenge owners, the results blew us away! While the project descriptions might be a bit less accessible to the public than some from past hackdays, the approaches and results certainly correspond to a present need in the energy industry and comfort us in the conviction that hackathons and collaborative work with Open Data do support high-end innovation.
We were also very lucky to welcome the team of Campus 21 who harvested the visions of some of the participants for the future of Open Energy Data. See you all next year!
The 13 projects developed during the hackdays District Heating Optimisation   Decrease gas peak boiler runtime due to better storage operation: heat demand forecast, improved storage control, better storage operation. PV self-consumption optimization Evaluate and optimize trade-offs in the design of battery storage for PV systems, so our customer can select, whether they want the most economical battery solution or maximise their autarky. Our tools calculate the maximized economic benefit over lifetime. Read your own Smart Meter Read your Smart Meter through the local Customer Information Interface (CII) and visualize your consumption. Design a dashboard with the most useful information. Cheapest Charging around In order to develop the GIS platform of the Swiss Federal Office of Energy (SFOE) further: Add price information to the charging stations and find the cheapest option around for electric car drivers. Energy Data Visualization     Creating a platform for strategic decision making based on data from the Energy Science Center of ETH Zürich. e-mobility behavior analysis We analysed the charging patterns of private vs public e-cars charging stations. This could provide good hints for a further automated customer segmentation, help prediction of behavior changes for the load-curve vs renewable electricity production & help customers optimize their charging habits. Empower the People with Smart Meter Data Smart Meter Additional Use Cases: Novel energy certificate assesses where and how strongly building / user behaviour causes deviation from theoretical / optimum behaviour. ML Wind Power-Prediction Machine Learning Wind Turbine Power Curve Prediction: we compared constructor provided production projections with actual production curves with the goal to improve site-specific  performance prediction of wind turbines. – Development of machine learning algorithms  (or tools/aps) for improved site-specific  performance prediction of wind turbines. – Development of alternative algorithms e.g. Artificial Neural Networks – Inputs: wind velocity, turbulence intensity, shear factor (alpha) Put CH on the Electricity Map Help meet the Paris Convention goals to achieve net 0 by 2050, less than 2 tonnes CO2 per person! We want to raise awareness around energy use and consumption by putting Switzerland on the map at electricitymap.org and put its open data API to use. Distributed analytics for asset management The goal is to create a decision support tool for asset managers, using AI to predict how power transformers will fail, and what to watch out for. Anomaly Detection in Smart Meter Data We developed EDA and algorithms for the Anomaly Detection in Smart Meter Data challenge. We developed several approaches for detecting anomalous days based on mean and std of the readings during the day and for detecting single anomalous readings. These models can be integrated in the second part  of the challenge MeterOS: Smart Meter Anomaly Detection Create a model for Smartmeter Anomaly detector and their visualization. Unleashing the Swiss Smartmeter’s CII Empower citizens to use their energy data. Using the smartmeter’s CII beyond visualisation to steer local consumption.We developed a concept and PoC roadmap to provide a “universal” adapter from smart meters to home IoT platforms.