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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

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Wei Lee Woon

    1. Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

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  2. Zeyar Aung

    1. Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

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  3. Oliver Kramer

    1. Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

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  4. Stuart Madnick

    1. MIT Sloan School of Management, Cambridge, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10691)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: DARE 2017.

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About this book

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.

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Keywords

Table of contents (11 papers)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration

    • Diana Manjarres, Ricardo Alonso, Sergio Gil-Lopez, Itziar Landa-Torres
    Pages 1-12

  3. Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions

    • Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown
    Pages 13-25

  4. Multi-objective Optimization for Power Load Recommendation Considering User’s Comfort

    • Jaroslav Loebl, Helmut Posch, Viera Rozinajová
    Pages 26-32

  5. An Approach for Erosion and Power Loss Prediction of Wind Turbines Using Big Data Analytics

    • Dina Fawzy, Sherin Moussa, Nagwa Badr
    Pages 33-46

  6. Minimizing Grid Interaction of Solar Generation and DHW Loads in nZEBs Using Model-Free Reinforcement Learning

    • Adhra Ali, Hussain Kazmi
    Pages 47-58

  7. Improving Time-Series Rule Matching Performance for Detecting Energy Consumption Patterns

    • Maël Guillemé, Laurence Rozé, Véronique Masson, Cérès Carton, René Quiniou, Alexandre Termier
    Pages 59-71

  8. Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis

    • Mehmet Baris Ozkan, Umut Guvengir, Dilek Küçük, Ali Unver Secen, Serkan Buhan, Turan Demirci et al.
    Pages 72-82

  9. Identifying Representative Load Time Series for Load Flow Calculations

    • Janosch Henze, Tanja Kneiske, Martin Braun, Bernhard Sick
    Pages 83-93

  10. Scalable Gaussian Process Models for Solar Power Forecasting

    • Astrid Dahl, Edwin Bonilla
    Pages 94-106

  11. Wind Speed Forecasting Using Statistical and Machine Learning Methods: A Case Study in the UAE

    • Khawla Al Dhaheri, Wei Lee Woon, Zeyar Aung
    Pages 107-120

  12. NWP Ensembles for Wind Energy Uncertainty Estimates

    • Alejandro Catalina, José R. Dorronsoro
    Pages 121-132

  13. Back Matter

    Pages 133-133
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Other volumes

  1. Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy

Editors and Affiliations

  • Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

    Wei Lee Woon, Zeyar Aung

  • Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

    Oliver Kramer

  • MIT Sloan School of Management, Cambridge, USA

    Stuart Madnick

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