Fr. 70.00

Data Analytics for Renewable Energy Integration - Third ECML PKDD Workshop, DARE 2015, Porto, Portugal, September 11, 2015. Revised Selected Papers

English · Paperback / Softback

Shipping usually within 6 to 7 weeks

Description

Read more

This bookconstitutes revised selected papers from the third ECML PKDD Workshop on DataAnalytics 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 forinclusion in this book.

List of contents

Imitative learningfor online planning in microgrids.- A novel central voltage-control strategyfor smart LV distribution networks.- Quantifying energy demand in mountainousareas.- Performance analysis of data mining techniques for improving theaccuracy of wind power forecast combination.- Evaluation of forecasting methodsfor very small-scale networks.- Classification cascades of overlapping featureensembles for energy time series data.- Correlation analysis for determiningthe potential of home energy management systems in Germany.- Predicting hourlyenergy consumption. Can regression modeling improve on an autoregressivebaseline.- An OPTICS clustering-based anomalous data filtering algorithm forcondition monitoring of power equipment.- Argument visualization and narrativeapproaches for collaborative spatial decision making and knowledgeconstruction: A case study for an offshore wind farm project.

Summary

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.

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.