Data Analytics for Renewable Energy Integration 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers / [electronic resource] :
edited by Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick.
- 1st ed. 2017.
- VII, 137 p. 58 illus. online resource.
- Lecture Notes in Artificial Intelligence, 10097 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 10097 .
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".
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.
9783319509471
10.1007/978-3-319-50947-1 doi
Artificial intelligence.
Data mining.
Renewable energy sources.
Computer science--Mathematics.
Energy policy.
Energy and state.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Renewable Energy.
Mathematics of Computing.
Energy Policy, Economics and Management.
Q334-342 TA347.A78
006.3
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".
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.
9783319509471
10.1007/978-3-319-50947-1 doi
Artificial intelligence.
Data mining.
Renewable energy sources.
Computer science--Mathematics.
Energy policy.
Energy and state.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
Renewable Energy.
Mathematics of Computing.
Energy Policy, Economics and Management.
Q334-342 TA347.A78
006.3