Computational Intelligence and Sustainable Energy: Case Studies and Applications

TitleComputational Intelligence and Sustainable Energy: Case Studies and Applications
Publication TypeTechnical Report
Year of Publication2010
AuthorsKramer, O.
Other Numbers3043
Abstract

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.

Acknowledgment

This work was partially funded by the Deutscher Akademischer Austausch Diesnst (DAAD) through a postdoctoral fellowship.

URLhttp://www.icsi.berkeley.edu/pubs/techreports/TR-10-010.pdf
Bibliographic Notes

ICSI Technical Report TR-10-010

Abbreviated Authors

O. Kramer

ICSI Research Group

Other

ICSI Publication Type

Technical Report