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 /
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 / [electronic resource] :
edited by Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick.
- 1st ed. 2017.
- X, 133 p. 49 illus. online resource.
- Lecture Notes in Artificial Intelligence, 10691 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 10691 .
Imitative learning for online planning in microgrids -- A novel central voltage-control strategy for smart LV distribution networks -- Quantifying energy demand in mountainous areas -- Performance analysis of data mining techniques for improving the accuracy of wind power forecast combination -- Evaluation of forecasting methods for very small-scale networks -- Classification cascades of overlapping feature ensembles for energy time series data -- Correlation analysis for determining the potential of home energy management systems in Germany -- Predicting hourly energy consumption. Can regression modeling improve on an autoregressive baseline -- An OPTICS clustering-based anomalous data filtering algorithm for condition monitoring of power equipment -- Argument visualization and narrative approaches for collaborative spatial decision making and knowledge construction: A case study for an offshore wind farm project.
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.
9783319716435
10.1007/978-3-319-71643-5 doi
Artificial intelligence.
Computer networks .
Data mining.
Renewable energy sources.
Energy policy.
Energy and state.
Artificial Intelligence.
Computer Communication Networks.
Data Mining and Knowledge Discovery.
Renewable Energy.
Energy Policy, Economics and Management.
Q334-342 TA347.A78
006.3
Imitative learning for online planning in microgrids -- A novel central voltage-control strategy for smart LV distribution networks -- Quantifying energy demand in mountainous areas -- Performance analysis of data mining techniques for improving the accuracy of wind power forecast combination -- Evaluation of forecasting methods for very small-scale networks -- Classification cascades of overlapping feature ensembles for energy time series data -- Correlation analysis for determining the potential of home energy management systems in Germany -- Predicting hourly energy consumption. Can regression modeling improve on an autoregressive baseline -- An OPTICS clustering-based anomalous data filtering algorithm for condition monitoring of power equipment -- Argument visualization and narrative approaches for collaborative spatial decision making and knowledge construction: A case study for an offshore wind farm project.
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.
9783319716435
10.1007/978-3-319-71643-5 doi
Artificial intelligence.
Computer networks .
Data mining.
Renewable energy sources.
Energy policy.
Energy and state.
Artificial Intelligence.
Computer Communication Networks.
Data Mining and Knowledge Discovery.
Renewable Energy.
Energy Policy, Economics and Management.
Q334-342 TA347.A78
006.3