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 goal of better economy, efficiency, reliability, and security can be achieved. In other words, data analytics is an essential tool that can help to imbue the smart grid with “smartness”.

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 Authors are invited to submit their original and unpublished research contributions to this workshop in areas relevant to the application of data analytics for renewable energy integration including but not limited to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:
Scope

- Data analytics for renewable energy sources
- Smart Grid applications of data analytics
- Data analytics for power generation, transmission, and distribution
- 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
- Smart grid cyber security
- Customer profiling and smart billing
- Parallel and distributed data analytics for renewable energy integration
- Big data and cloud-based analytics for renewable energy integration

Two types of submissions are invited:

- Full papers (Maximum 14 pages, including title page and bibliography).
- Short position papers (Maximum 4 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 by Springer-Verlag in a volume of the Lecture Notes in Artificial Intelligence (LNCS/LNAI) series (indexed in ISI Proceedings, DBLP. Ulrich's, EI-Compendex, SCOPUS, Zentralblatt Math, MetaPress, Springerlink).

Key Dates
- Submission Deadline: 6th of July, 2014
- Notification to Authors: 25th of July 2014
- Camera-ready Deadline: 1st of August 2014
- Workshop day: 19th of September 2014

For manuscript submission, please use the EasyChair site at:
https://www.easychair.org/conferences/?conf=dare20140

Manuscripts should adhere to format guidelines of ECML/PKDD (Springer LNCS/LNAI) available at http://www.springer.de/comp/lncs/authors.html, as this will be the required format for accepted papers.

More details regarding the workshop are available from the website:
http://dare2014.dnagroup.org