Publication Details
Title: Computational Intelligence and Sustainable Energy: Case Studies and Applications
Author: O. Kramer
Group: ICSI Technical Reports
Date: November 2010
PDF: http://www.icsi.berkeley.edu/pubs/techreports/TR-10-010.pdf
Overview:
Sustainability is of great importance due to increasing demands and limited resources. Many problem classes in sustainable energy systems are data mining, optimization, and control tasks. In this work we demonstrate how techniques from computational intelligence can help in solving important tasks in sustainable energy systems. We will show how statistically sound wind models can be estimated with kernel smoothing methods. Radial basis functions will be employed for wind resource visualization. Support vector machines turn out to be successful in forecasting wind energy. Monitoring of high-dimensional wind time series is possible with a self-organizing map approach. Slow driving features in wind time series can be detected with slow feature analysis. Last, we will demonstrate how a learning classifier system evolves control rules for a virtual power plant with a simple demand side management model.
Acknowledgements:
This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship.
Bibliographic Information:
ICSI Technical Report TR-10-010
Bibliographic Reference:
O. Kramer. Computational Intelligence and Sustainable Energy: Case Studies and Applications. ICSI Technical Report TR-10-010, November 2010
Author: O. Kramer
Group: ICSI Technical Reports
Date: November 2010
PDF: http://www.icsi.berkeley.edu/pubs/techreports/TR-10-010.pdf
Overview:
Sustainability is of great importance due to increasing demands and limited resources. Many problem classes in sustainable energy systems are data mining, optimization, and control tasks. In this work we demonstrate how techniques from computational intelligence can help in solving important tasks in sustainable energy systems. We will show how statistically sound wind models can be estimated with kernel smoothing methods. Radial basis functions will be employed for wind resource visualization. Support vector machines turn out to be successful in forecasting wind energy. Monitoring of high-dimensional wind time series is possible with a self-organizing map approach. Slow driving features in wind time series can be detected with slow feature analysis. Last, we will demonstrate how a learning classifier system evolves control rules for a virtual power plant with a simple demand side management model.
Acknowledgements:
This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship.
Bibliographic Information:
ICSI Technical Report TR-10-010
Bibliographic Reference:
O. Kramer. Computational Intelligence and Sustainable Energy: Case Studies and Applications. ICSI Technical Report TR-10-010, November 2010