Call for papers

Data Analytics and Machine Learning for the Energy Internet

Energy Internet goes beyond smart grid by integrating multiple energy systems, combining cyber-physical-social systems, and creating innovative business modes for the whole energy system. Widely installed sensors enable the collection of various monitoring data of the energy system, which can help different participators to have a better understanding of how the power and energy system works. Meanwhile, data analytics and machine learning techniques such as deep learning, transfer learning, graphical models, spares representation, etc., have been greatly and considerably developed in recent years. It is important to figure out how to apply these state-of-the-art techniques to the Energy Internet. Even though increasing efforts from both academic and industrial worlds have been adopted to model, analyze, diagnose, control, operate, and plan the power and energy systems from data-driven perspective, we believe that there is still a long way to go for the research on data analytics and machine learning for the Energy Internet.

This Special Issue in International Transactions on Electrical Energy Systems aims to publish original and innovative works related to data analytics and machine learning power and energy system. This Special Issue solicits original work that is not under consideration for publication in other avenues. Topics of interest include, but are not limited to:

Ÿ Efficient data compression, cleaning, and management algorithms;

Ÿ Analysis and visualization of smart meter data, phasor measurement unit (PMU) data, etc.;

Ÿ Anomaly detection and monitoring for energy consumption, generation, and equipment;

Ÿ Data fusion for power, gas, heat, and cooling integrated energy systems;

Ÿ Forecasting of loads, price, and outputs of renewable energy;

Ÿ Data-driven power flow modeling and network topology identification;

Ÿ Data privacy and security for various participators in the power and energy system;

Ÿ Machine learning for optimal decisions such as home and building energy management, price design, unit commitment, power system planning;

Ÿ Distributed and edge computing for big data applications in the Energy Internet; 

Submitted papers should have original contributions to data analytics and machine learning for the Energy Internet. Survey/review papers are also welcome.

Prospective authors should refer to the website blow for guidelines on content and formatting of submissions.

https://onlinelibrary.wiley.com/page/journal/20507038/homepage/forauthors.html

Please submit via https://mc.manuscriptcentral.com/itees. The Manuscript Type “Special Issue Paper” should be used. Please also ensure that you indicate in the covering letter that your submission relates to this Special Issue and answer “Yes” to the submission question asking if the submission is for a Special Issue. Authors should also provide the Special Issue Title in the “Special Issue Information” filed in the submission steps.

Editor-in-chief:

Prof. Chongqing Kang, Tsinghua University, China

Editorial Board:

Dr. Yi Wang (Lead editor), ETH Zurich, Switzerland

(yiwang@eeh.ee.ethz.ch)

Prof. Nima Amjady, Semnan University, Iran

(amjady@semnan.ac.ir)

Prof. Ning Zhang, Tsinghua University, China

(ningzhang@tsinghua.edu.cn)

Prof. Guido Carpinelli (Guest editor), University of Naples Federico II, Italy

(guido.carpinelli@unina.it)

Prof. Canbing Li (Guest editor), Hunan University, China

(licanbing@qq.com)

Dr. Mingyang Sun (Guest editor), Imperial College London, UK

(mingyang.sun11@imperial.ac.uk)

Prof. Gregor Verbic (Guest editor), The University of Sydney, Australia

(gregor.verbic@sydney.edu.au)

Important Dates: 

September 1, 2019: Deadline for full paper submission

January 31, 2020: Notification of final decisions

March 30, 2020: Publication materials due