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CEMiSG 2016 : 3rd International Workshop on Computational Energy Management in Smart Grids


When Jul 25, 2016 - Jul 25, 2016
Where Vancouver - Canada
Submission Deadline Jan 15, 2016
Notification Due Mar 15, 2016
Final Version Due Apr 25, 2016
Categories    energy management   computational intelligence   smart grids   neural networks

Call For Papers


Stefano Squartini, Università Politecnica delle Marche, Italy
Derong Liu, Chinese Academy of Sciences, China
Francesco Piazza, Università Politecnica delle Marche , Italy
Dongbin Zhao, Chinese Academy of Sciences, China
Haibo He, University of Rhode Island, USA


The sustainable usage of energy resources is actually an issue that humanity and technology have been seriously facing in the last decade, as a consequence of the higher and higher energy demand worldwide and the strong dependence on oil-based fuels. This shoved the scientists and technicians worldwide to intensify their studies on renewable energy resources, especially in the Electrical Energy sector. At the same time, a remarkable increment of the complexity of the electrical grid has been also registered at diverse levels in order to include variegated and distributed generation and storage sites, resulting in strong engineering challenges in terms of energy distribution, management and system maintenance. This yielded in a flourishing scientific literature on sophisticated algorithms and systems aimed at introducing intelligence within the electrical energy grid with several effective solutions already available in the market. These efforts have also recently cross-fertilized both research and development of commercial products for other grid types, as the smart water and natural gas grids, which have been registering an increasing interest in the last five years.

The many different needs coming from heterogeneous grid customers, at diverse grid level, and the different peculiarities of energy sources to be included in the grid itself, makes the task challenging and multi-faceted. Along this same direction, a big variety of interventions can be applied into the grid to increase the inherent degree of automation, optimal functioning, security and reliability, thus increasing the engineering appeal of the issue. A multi-disciplinary coordinated action is therefore required to the scientific communities operating in the Electrical and Electronic Engineering, Computational Intelligence, Digital Signal Processing and Telecommunications research fields to provide adequate technological solutions, having in mind the more and more stringent constraints in terms of environmental sustainability.

Focalizing to the interests of our scientific community, the organizers of this Workshop wants to explore the new frontiers and challenges within the Computational Intelligence research area, including Neural Networks based solutions, for the optimal usage and management of energy resources in Smart Grid scenarios. Indeed, the recent adoption of distributed sensor networks in many grid contexts enabled the availability of data to be used to develop suitable expert systems with the aim of supporting the humans in dealing with the complex problems in grid management, from multiple applicative perspectives. Related research is undoubtedly already florid, but many open issues need to be studied and innovative intelligent systems investigated.

By moving from the success obtained by the CEMiSG2014 Workshop organized within the IJCNN2014 conference in Beijing (China) and by the CEMiSG2015 Workshop organized within the IJCNN2015 conference in Killarney (Ireland), the third edition of the CEMiSG Workshop is still targeted to propose a proficient discussion table for scientists joining the IJCNN2016 conference at the WCCI2016.


Workshop topics include, but are not limited to:

Computational Intelligence for Smart Grids Applications
Neural Networks based algorithms for Complex Energy Systems
Soft Computing based Algorithms in Energy Applications
Expert Systems for Smart Grid Optimization
Smart Grids and Big Data
Computational Intelligence for Vehicle to Grid
Automatic Fault Detection Algorithms in Smart Grid scenarios
Computational methods for Smart Grid Self-Healing
Learning-based Control of Renewable Energy Generators
Smart Building Energy Management
Deep Neural Networks for Energy Efficiency
Computational Intelligence for Energy Internet Management
Energy Resource Allocation and Task Scheduling
Short/Long-term Load Forecasting
Demand-side Management
Learning Systems for Smart AMIs
Neural Networks for Time Series Prediction in Smart Grids
Non-Intrusive Load Monitoring
Hybrid Battery Management


Prospective authors are invited to submit papers according to the specifications of IJCNN2016. Manuscripts will be submitted through the IJCNN 2016 paper submission website and will be subject to the same peer-review review procedure as the IJCNN 2016 regular papers. Accepted contributions will be part of the IJCNN conference proceedings.

The paper submission deadline is January 15, 2016.

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