Modelling the end-user Swiss electricity consumption
Silvia holds an MSc in Computer Science from EPFL and a PhD in Computer Science from the University of York, UK. She has been a senior research fellow at the University of Trento and later at Politecnico di Milano, Italy. Here, she had the chance to work on Marie Curie and ERC projects relating to natural language processing. From 2012 to 2019, she was a Senior Manager and NLP expert at ELCA Informatique Switzerland, whose AI department she helped create and expand. Silvia joined the Swiss Data Science Center in 2019 and is currently its Chief Transformation Officer, in charge of the team leading organizations to digital transformation.
Roberto holds an M.Sc. and a Ph.D. in Particle Physics from the University of Torino, Italy. He has worked for several years in fundamental research as a senior fellow and data scientist at the CERN Experimental Physics division and on a research project supported by the Belgian National Fund for Scientific Research (FNRS). In 2018 he moved to EPFL to work on data mining and Machine Learning techniques for the built environment and renewable energies. He has started and led multiple collaborations with academic and industry partners in the energy domain. Roberto joined the SDSC in September 2021 as a Principal Data Scientist with the mission of accompanying industries, NGOs and international organizations through their data science journey.
Alessandro holds an M.Sc. in Applied Mathematics from EPFL with a minor in Data Science. After his studies, he joined the SDSC as Data Scientist in April 2022, where he closely works with the academic community to enlarge and support the use of data science. Over the years he worked on a variety of topics, from extreme events modeling to time series representation. His main interest lies in the application of machine learning to the energy sector.
Context
The electricity and gas crises in Europe and Switzerland during winter 2022 required exceptional measures to meet the demand and to cope with soaring energy prices. Due to this, Swiss policy makers and legislators were in urgent need of a near to real-time estimates of the national electricity consumption. In Switzerland, only the vertical load seen from the transmission network and the total national electricity consumption are published, and with major delays. Furthermore, at the end of August 2022 the Swiss government launched an energy saving campaign, calling for a quantification of the changes in electricity consumption habits of the Swiss population.
Objectives
Using a model (Generalized Additive Model or GAM) which learns from the Swiss historic national consumption provided by Swissgrid, we could predict the national electricity demand using as input also calendar and meteorological data (from MeteoSwiss). Moreover, we developed a bottom-up methodology to quantify changes in electricity consumption by the end-users. From individual load curves from smart meters of Distribution Network Operators, we extrapolate the load to the national level using scaling factors and corrections.
Benefits
The models to forecast the electricity demand at national level and to quantify the reduction of consumption have been integrated into the SFOE Energy Dashboard. The results of this project allowed to monitor the real-time electricity consumption and to quantify up to 6% energy saving by end-users after the launch of the Federal campaign. These examples prove how data science can support decision-making processes and can measure the effectiveness of large-scale policies implementation.
Notes
Special thanks to the OFEN Digital Innovation Office, particularly to Matthias Galus and Fabian Heymann, for the fruitful exchanges and to Nicolas Charton and Yves Baudet from E-CUBE Strategy Consultants SA for their shared advice and expertise during the entire project.
References / Links
- SFOE | Energy Dashboard Switzerland
- Swiss Data Science Center | The SDSC collaborates with the SFOE to push further digital innovation in the energy sector
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