Tuesday, 1 May 2018

CFP: IEEE TETCI Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities (May 15)


  By 2050, more than half the world’s population is expected to live in urban regions. This rapid expansion of population in the cities of the future will lead to increasing demands on various infrastructures; the urban economics will play a major role in national economics. Cities must be competitive by providing smart functions to support high quality of life. There is thus an urgent need to develop smart cities that possess a number of smart components. Among them, smart energy is arguably the first infrastructure to be established because almost all systems require energy to operate.
  Smart energy refers to energy monitoring, prediction, use or management in a smart way. In smart cities, smart energy applications include smart grids, smart mobility, and smart communications. While realizing smart energy is promising to smart cities, it involves a number of challenges.
  By using smart grid technologies, distributed power supply is replacing conventional centralized schemes, leading to regional aggregation of energy that must consider the interests of many grid participants. With the increasing penetration of electric vehicles (EVs), EV charging stations must consider many parameters and objectives to optimize the charging schedule. To make transportation or communications infrastructures go green, renewable energy sources (RESs) are often integrated into the whole system as part of power supply; a robust prediction for both power load and energy production becomes necessary for later energy management in response to intermittent power supply from RESs.
  Because of the uncertainty of environments, complexity of the problem of interest, or multiplicity of objectives that must be achieved, conventional optimization methods using deterministic search algorithms cannot well address these challenges. By contrast, stochastic optimization can be useful for handling uncertainty; adaptive learning based on, for example, human behaviors, available resources, network capacity, or collected data can be a solution to complex problems; evolutionary computation can be applied to solve problems with many objectives. Computational Intelligence (CI) thus serves as a useful tool for addressing aforementioned difficulties.


This Special Issue aims to provide in-depth CI technologies that enable smart energy applications to smart cities. Topics of interest include, but are not limited to:

  • Evolutionary computation for smart grids in consideration of many objectives, including energy management system, demand-side management, demand response, advanced metering infrastructure, and behind-the-meter applications.
  • Stochastic optimization for smart mobility in consideration of system uncertainty, with a primary focus on power scheduling for Internet of EVs or green public transportation.
  • Intelligent algorithms for smart communications pertaining to Internet of Things, machine-to-machine communications, vehicle-to-grid communications, and vehicle-to-infrastructure communications under the framework of green communications.
  • Machine/deep learning for renewable energy forecasting or power load forecasting.
  • Survey papers on CI for smart energy applications.


  •  Submission deadline: May 15, 2018.
  •  Notification due date: October 1, 2018.
  •  Final version due date: November 1, 2018.


  Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website:https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Computational Intelligence for Smart Energy Applications to Smart Cities” and clearly marking “Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities” as comments to the Editor-in-Chief.


  • Wei-Yu Chiu, National Tsing Hua University, Taiwan (wychiu@ee.nthu.edu.tw)
  • Hongjian Sun, Durham University, UK
  • Chao Wang, Tongji University, China
  • Athanasios V . V asilakos, Lulea University of Technology, Sweden

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