Data Analytics for Renewable Energy Integration [electronic resource] : Third ECML PKDD Workshop, DARE 2015, Porto, Portugal, September 11, 2015. Revised Selected Papers / edited by Wei Lee Woon, Zeyar Aung, Stuart Madnick.

Contributor(s): Woon, Wei Lee [editor.] | Aung, Zeyar [editor.] | Madnick, Stuart [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 9518Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: VII, 155 p. 94 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319274300Subject(s): 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 EconomicsAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

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.

There are no comments on this title.

to post a comment.

Implemented and Maintained by Biju Patnaik Central Library.
For any Suggestions/Query Contact to library or Email: library@nitrkl.ac.in OR bpcl-cir@nitrkl.ac.in. Ph:91+6612462103
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha