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Data Analytics for Renewable Energy Integration: 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers 1st ed. 2017 [Pehme köide]

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  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 58 Illustrations, black and white; VII, 137 p. 58 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 10097
  • Ilmumisaeg: 19-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319509462
  • ISBN-13: 9783319509464
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Data Analytics for Renewable Energy Integration: 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers 1st ed. 2017Suurem pilt
  • Formaat: Paperback / softback, 137 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 58 Illustrations, black and white; VII, 137 p. 58 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 10097
  • Ilmumisaeg: 19-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319509462
  • ISBN-13: 9783319509464
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319509464.html
Märksõnad:

This book constitutes revised selected papers from the 4th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2016, held in Riva del Garda, Italy, in September 2016.
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.

Locating Faults in Photovoltaic Systems Data.- Forecasting of Smart
Meter Time Series Based on Neural Cybersecurity for Smart Cities: A Brief
Review.- Machine Learning Prediction of Photovoltaic Energy from Satellite
Sources.- Approximate Probabilistic Power Flow.- Dealing with Uncertainty: An
Empirical Study on the Relevance of Renewable Energy Forecasting Methods.-
Measuring Stakeholders Perceptions of Cybersecurity for Renewable Energy
Systems.- Selection of Numerical Weather Forecast Features for PV Power
Predictions with Random Forests.- Evolutionary Multi-Objective Ensembles
forWind Power Prediction.- A Semi-Automatic Approach for Tech Mining and
Interactive Taxonomy Visualization.- Decomposition of Aggregate Electricity
Demand into the Seasonal-Thermal Components for Demand-Side Management
Applications in "Smart Grids".