posted by user: zeyarag || 6780 views || tracked by 11 users: [display]

DARE 2016 : ECML/PKDD 2016, 4th International Workshop on Data Analytics for Renewable Energy Integration


When Sep 23, 2016 - Sep 23, 2016
Where Riva del Garda, Italy
Submission Deadline Jul 18, 2016
Notification Due Jul 25, 2016
Final Version Due Aug 8, 2016
Categories    renewable energy   data mining   machine learning   smart grid

Call For Papers

DARE 2016: ECML/PKDD 2016, 4th International Workshop on Data Analytics for Renewable Energy Integration

[[To be published as a Springer’s Lecture Notes in Artificial Intelligence (LNAI) Volume]]

Concerns about climate change, energy security and dwindling fossil fuel reserves are stimulating ever increasing interest in the generation, distribution and management of renewable energy. While a lot of attention has been devoted to generation technologies, an equally important challenge is the integration of energy extracted from renewable resources into existing electricity distribution and transmission systems. Renewable energy resources like wind and solar energy are often spatially distributed and inherently variable, necessitating the use of computing techniques to predict levels of supply and demand, coordinate electricity distribution and manage the operations of energy storage facilities.

A key element of the solution to this problem is the concept of a “Smart Grid”. There is no standard definition but a smart grid is broadly perceived as an evolved form of the traditional electricity grid where advanced techniques such as Information and Communication Technology (ICT) are used extensively to detect, predict and intelligently respond to events that may affect the supply of electricity.

Data analytics is a science that encompasses data mining, machine learning and statistical methods, and which focuses on cleaning, transforming, modeling and extracting actionable information from large, complex data sets. A smart grid generates a large amount of data from its various components, examples of which include renewable energy generators and smart meters; the potential value of this data is huge but exploiting this value will be almost impossible without the use of proper analytics. With the application of systematic analytics on the smart grid’s data, its goals of better economy, efficiency, reliability, and security can be achieved. A further consequence of this process is the steady growth in the complexity and connectedness of critical energy infrastructure. This trend, coupled with the rapid growth in computing power and an increasingly diverse threat landscape, has led to pressing concerns about the vulnerability of these installations to cybersecurity attacks from a range of state and non-state actors. It seems certain that intelligent algorithms and data analytics will be an important part of the solution if these problems are to be effectively countered.

The focus of this workshop is to study and present the use of various data analytics techniques in the different areas of renewable energy integration. Authors are invited to submit their original and unpublished research contributions to this workshop in areas relevant to the application of data analytics for renewable energy integration including but not limited to the following:
• Data analytics for renewable energy sources
• Smart Grid applications of data analytics
• Data analytics for power generation, transmission, and distribution
• Smart grid cyber security
• Intrusion detection
• SCADA/DCS data analytics
• Fault detection, classification, location, and diagnosis
• Power quality detection
• Power system state estimation
• Load forecasting, wind power forecasting, and PV power forecasting
• Islanding detection
• Demand response
• Customer profiling and smart billing
• Parallel and distributed data analytics for renewable energy integration
• Big data and cloud-based analytics for renewable energy integration

Paper Submission
Two types of submissions are invited:
• Full papers (Maximum 12 pages, including title page and bibliography)
• Short position papers (Maximum 6 pages, including title page and bibliography)
Submitted papers will be peer-reviewed and selected on the basis of these reviews.

Accepted papers will be presented at the workshop and published in the workshop post-proceedings as a Springer’s Lecture Notes in Artificial Intelligence (LNAI) volume.

(Note: The past two last years’ DARE 2014 and DARE 2015 workshop post-proceedings were also published as Springer’s LNAI Volumes 8817 and 9518 respectively.)

For manuscript submission, please use the EasyChair site at:

Manuscripts should adhere to the guidelines of Springer LNCS/LNAI format:

Key Dates
• Workshop paper submission deadline: Monday, July 18, 2016
• Workshop paper acceptance notification: Monday, July 25, 2016
• Workshop paper camera-ready deadline: Monday, August 8, 2016
• Workshop day: Friday, September 23, 2016

More details regarding the workshop are available from the website:

Related Resources

MLDM 2023   18th International Conference on Machine Learning and Data Mining
ICSREE 2023   2023 8th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2023)--SCI
IJCNN 2023   International Joint Conference on Neural Networks
ICEEEP 2023   2023 7th International Conference on Energy Economics and Energy Policy (ICEEEP 2023)
FAIML 2023   2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2023)
AREEE 2023   2023 4th Asia Conference on Renewable Energy And Environmental Engineering (AREEE 2023)
PAKDD 2023   The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining
ICCGE 2023   2023 12th International Conference on Clean and Green Energy (ICCGE 2023)
ICSREE--SCI 2023   2023 8th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2023)--SCI
CVPR 2023   The IEEE/CVF Conference on Computer Vision and Pattern Recognition