3rd International Workshop on Computational Energy Management in Smart Grids (CEMiSG 2016)
When : 2016-07-25
Where : Canada
Submission Deadline : 2016-01-31
Categories : Soft Computing , Computer Science & Information Technology
3rd International Workshop on Computational Energy Management in Smart Grids (CEMiSG 2016)
July 25, 2016
Canada
Call For Papers
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.
Topics
Computational Intelligence for Smart Grids ApplicationsNeural 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