posted by user: zeyarag || 764 views || tracked by 1 users: [display]

DARE 2018 : ECMLPKDD 2018, 6th International Workshop on Data Analytics for Renewable Energy Integration


When Sep 14, 2018 - Sep 14, 2018
Where Dublin, Ireland
Submission Deadline Jul 2, 2018
Notification Due Jul 23, 2018
Final Version Due Aug 6, 2018
Categories    renewable energy   data mining   machine learning   artificial intelligence

Call For Papers

DARE 2018: 6th International Workshop on Data Analytics for Renewable Energy Integration (in Conjunction with ECMLPKDD 2018)

Dublin, Ireland, September 2018

[The workshop proceedings will be published as a volume in Springer’s Lecture Notes in Artificial Intelligence (LNAI) series.]

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 energy 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 power and solar energy are often spatially distributed and inherently variable, necessitating the use of computing techniques to predict levels of supply and demand, coordinate power distribution, manage the operations of storage facilities and detect faults and cybersecurity threats. These challenges rely heavily on the development and appropriate use of techniques, systems, and algorithms which can effectively handle large quantities of data to detect, predict and respond intelligently to events affecting the generation and supply of energy from renewable energy resources.

Data analytics is the science that encompasses machine learning (including deep learning), and big data, focusing on cleaning, transforming, modelling and extracting actionable information from large, complex data sets. A renewable energy system generates large amounts of data from various components such as smart meters and relays. The potential value of this data is huge, but exploiting this value will be almost impossible without the use of proper analytic techniques. With the systematic application of analytics and machine learning techniques on this data, better economy, efficiency, reliability, and security can be achieved.

This year, we would also like to highlight the integration of renewable energy in society as an additional component of this challenge. It is clear that the spread of renewable energy will require the increasing participation and support of end users. In this context, related topics include demand response, residential PV installations and even social media analytics in the context of building and measuring awareness of and attitudes towards renewable energy.

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 DARE 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 and machine learning
• Data analytics for power generation, transmission, and distribution
• Fault detection, classification, location, and diagnosis
• Smart grid cyber security
• Applications of Deep Learning
• Demand response
• Customer profiling and smart billing
• Load forecasting, wind power forecasting, and PV power forecasting
• Power quality detection
• Power system state estimation
• Social Media Analytics
• SCADA/DCS data analytics
• Islanding detection
• 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 proceedings – as a volume in Springer’s Lecture Notes in Artificial Intelligence (LNAI) series.

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 2, 2018
• Workshop paper acceptance notification: Monday July 23, 2018
• Workshop paper camera-ready deadline: Monday August 6, 2018
• Workshop day: 10th or 14th of September 2018

[If the authors are concerned about completing their paper in time for the deadline, they can email to the organizers at: wlwoon(at)]

More details regarding the workshop are available from the website:

Note: DARE 2018 proceedings will be published in Springer’s Lecture Notes in Artificial Intelligence (LNAI) series. The DARE proceedings for the past four years (2014-2017) were published in LNAI series as volumes 8817, 9518, 10097, and 10691 respectively.

Related Resources

ACML 2018   The 10th Asian Conference on Machine Learning
VIcbd 2018   11th Annual Cloud & Big Data Analytics 2018
AAAI 2019   National Conference on Artificial Intelligence
Scopus/EI-ICRESG 2019   2019 4th International Conference on Renewable Energy and Smart Grid (ICRESG 2019)
WSDM 2019   WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining
ICERE--EI, Scopus 2019   2019 5th International Conference on Environment and Renewable Energy (ICERE 2019)--EI Compendex, Scopus
ISCSAI 2018   2018 International Symposium on Computer Science and Artificial Intelligence
IJIST 2018   The International Journal of Information Science & techniques
ICERE--EI Compendex, Scopus 2019   2019 5th International Conference on Environment and Renewable Energy (ICERE 2019)--EI Compendex, Scopus
ICDMML 2019   2019 International Conference on Data Mining and Machine Learning