Building power demand forecasting
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Oleg Valgaev
Oleg Valgaev was born and grew up in Moscow, Russia. He received his M.Sc. in electrical engineering from the Federal Institute of Technology (ETH), Zurich in 2014. In the same year he joined Austrian Institute of Technology where he is currently working as a research fellow. His research interests are power demand forecasting and time series analysis.
Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna
and
Friederich Kupzog
Friederich Kupzog was born in Cologne, Germany in 1979 and achieved the Diploma Engineer degree of electrical engineering and information technology at RWTH Aachen University of Technology in February 2006. He joined the Institute of Computer Technology at Vienna Technical University, Austria, in March 2006. In 2008 he achieved his PhD Degree. Until 2012, Dr. Kupzog was leading the research group “Energy & IT” at the Institute of Computer Technology, where he managed more than 15 research projects on Smart Grids together with industry, power grid operators and other research partners. Since 2012, he is Senior Scientist at the AIT Austrian Institute of Technology. His research interest lies in verification methods for networked smart grid systems. Dr. Kupzog holds lectures in smart grid related topics at TU Vienna, FH Technikum Wien as well as FH Salzburg and is active in national (ComForEn) and international (IEEE INDIN, IEEE IECON) scientific conference organisation. He was awarded the Austrian Smart Grid Pioneer Award together with his TU Vienna colleagues in 2010 and 2012.
Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna
Abstract
Buildings acting as flexible loads have been often proposed to mitigate the volatility of renewable energy sources. Thereby, an accurate short-term demand forecast is indispensable for effective demand side management. At the same time, standardized load profiles, commonly used in the distribution grid, are inadequate for load forecasting within building domain. For this PhD, project a novel short-term forecasting model is proposed for that domain. It considers not only residual load, but also scheduled demand response as well as the PV-generation of the building. Moreover, it is not building specific and is, therefore, suitable for area-wide application within building domain.
About the authors
Oleg Valgaev was born and grew up in Moscow, Russia. He received his M.Sc. in electrical engineering from the Federal Institute of Technology (ETH), Zurich in 2014. In the same year he joined Austrian Institute of Technology where he is currently working as a research fellow. His research interests are power demand forecasting and time series analysis.
Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna
Friederich Kupzog was born in Cologne, Germany in 1979 and achieved the Diploma Engineer degree of electrical engineering and information technology at RWTH Aachen University of Technology in February 2006. He joined the Institute of Computer Technology at Vienna Technical University, Austria, in March 2006. In 2008 he achieved his PhD Degree. Until 2012, Dr. Kupzog was leading the research group “Energy & IT” at the Institute of Computer Technology, where he managed more than 15 research projects on Smart Grids together with industry, power grid operators and other research partners. Since 2012, he is Senior Scientist at the AIT Austrian Institute of Technology. His research interest lies in verification methods for networked smart grid systems. Dr. Kupzog holds lectures in smart grid related topics at TU Vienna, FH Technikum Wien as well as FH Salzburg and is active in national (ComForEn) and international (IEEE INDIN, IEEE IECON) scientific conference organisation. He was awarded the Austrian Smart Grid Pioneer Award together with his TU Vienna colleagues in 2010 and 2012.
Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna
©2016 Walter de Gruyter Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Energy Informatics
- Special Issue
- Towards composition principles and fractal architectures in the context of smart grids
- Load flexibility in an industrial setting: Energy-aware test rig scheduling in the automotive industry
- Modeling of battery life optimal charging strategies based on empirical mobility data
- SG-rating – Putting values on smart grid architectures
- Building power demand forecasting
- Self-Portrayals of GI Junior Fellows
- Using cognitive models for behavioral assistance of humans
- Regular Paper
- Architecting safety-critical decision-support systems for nuclear emergency management
Articles in the same Issue
- Frontmatter
- Editorial
- Energy Informatics
- Special Issue
- Towards composition principles and fractal architectures in the context of smart grids
- Load flexibility in an industrial setting: Energy-aware test rig scheduling in the automotive industry
- Modeling of battery life optimal charging strategies based on empirical mobility data
- SG-rating – Putting values on smart grid architectures
- Building power demand forecasting
- Self-Portrayals of GI Junior Fellows
- Using cognitive models for behavioral assistance of humans
- Regular Paper
- Architecting safety-critical decision-support systems for nuclear emergency management
