Data Analytics for Renewable Energy Integration Third ECML PKDD Workshop, DARE 2015, Porto, Portugal, September 11, 2015. Revised Selected Papers / [electronic resource] : edited by Wei Lee Woon, Zeyar Aung, Stuart Madnick. - 1st ed. 2015. - Cham : Springer International Publishing : Imprint: Springer, 2015. - VII, 155 p. 94 illus. in color. online resource. - Lecture Notes in Computer Science, 9518 0302-9743 ; . - Lecture Notes in Computer Science, 9518 .

Imitative learning for online planning in microgrids -- A novel central voltage‐control strategy for smart LV distribution networks -- Quantifying energy demand in mountainous areas -- Performance analysis of data mining techniques for improving the accuracy of wind power forecast combination -- Evaluation of forecasting methods for very small‐scale networks -- Classification cascades of overlapping feature ensembles for energy time series data -- Correlation analysis for determining the potential of home energy management systems in Germany -- Predicting hourly energy consumption. Can regression modeling improve on an autoregressive baseline -- An OPTICS clustering‐based anomalous data filtering algorithm for condition monitoring of power equipment -- Argument visualization and narrative approaches for collaborative spatial decision making and knowledge construction: A case study for an offshore wind farm project.

This book constitutes revised selected papers from the third ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2015, held in Porto, Portugal, in September 2015. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in this book.


10.1007/978-3-319-27430-0 doi

Computer science.
Renewable energy resources.
Computer science--Mathematics.
Data mining.
Artificial intelligence.
Renewable energy sources.
Alternate energy sources.
Green energy industries.
Energy industries.
Computer Science.
Artificial Intelligence (incl. Robotics).
Data Mining and Knowledge Discovery.
Renewable and Green Energy.
Mathematics of Computing.
Energy Economics.

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