ICT4SmartGrid 2020

ICT4SmartGrid 2020

The 1st IEEE International Workshop on Rising ICT Solutions for Smart Grids as Multi-energy Systems

This workshop aims to attract practitioners and researchers in all fields of ICT (e.g., Artificial Intelligence, Big Data, Internet-of-Things, Blockchain, etc…) applied to the domain of smart grids intended as Multi-energy Systems.

Date: 13 July 2020

Event: CETIC talks 

Domaine: CleanTech 

About project: GAC 

In the scenario of Smart Grids as part of next generation Smart Cities, multiple cutting-edge ICT technologies are playing a leading role. Among the others, Artificial Intelligence and Machine Learning algorithms (including Deep Learning Networks) provide a data-driven way to demand and load forecasting. Internet-of-Things architectures coupled with Big Data frameworks are beneficial to the efficient gathering and management of energy information. Latest Blockchain technologies promise to revolutionize the energy marketplace by introducing dynamic smart contracts. Simulation and co-simulation techniques (including agent-based simulations, hardware-in-the-loop) provide a basis for testing novel energy management policies at multiple spatio-temporal resolutions.

Hence, we are confident that our workshop theme will be of interest for a large scientific community of researchers attending next COMPSAC 2020

The concept of the smart grid is not limited only to introducing novel solutions for making electric distribution networks smarter; it also includes a holistic view of integrated Smart Multi-Energy Systems (SMESs), with the goal of planning the correct deployment of energy resources, including renewable ones. Developing such SMESs needs a strong multidisciplinary approach taking a multiplicity of heterogeneous factors into account, including the operational phase of energy systems, resources and consumption requests, people behaviours, energy network constrains (i.e. gas, heating and electricity networks).

Cetic will attend online and present the paper:
Impacts of size and history length on energetic community load forecasting: a case study

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