Data Analytics for Renewable Energy Integration [electronic resource] : 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers / edited by Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick.

Contributor(s): Woon, Wei Lee [editor.] | Aung, Zeyar [editor.] | Kramer, Oliver [editor.] | Madnick, Stuart [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 10097Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Description: VII, 137 p. 58 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319509471Subject(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:
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 for Wind 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".
In: Springer eBooksSummary: 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.
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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 for Wind 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".

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.

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