Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy : 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers / edited by Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick.

Format:

Book

Contributor:

Woon, Wei Lee, editor.

Aung, Zeyar, editor.

Kramer, Oliver, editor.

Madnick, Stuart, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 10691.

Lecture Notes in Artificial Intelligence ; 10691

Language:

English

Subjects (All):

Artificial intelligence.

Computer networks.

Data mining.

Renewable energy sources.

Energy policy.

Artificial Intelligence.

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Renewable and Green Energy.

Energy Policy, Economics and Management.

Local Subjects:

Artificial Intelligence.

Computer Communication Networks.

Data Mining and Knowledge Discovery.

Renewable and Green Energy.

Energy Policy, Economics and Management.

Physical Description:

1 online resource (X, 133 pages) : 49 illustrations.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

This book constitutes revised selected papers from the 5th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2017, held in Skopje, Macedonia, in September 2017. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response and many others.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-71643-5

9783319716435

Access Restriction:

Restricted for use by site license.