Our Aim
Telecommunication and data networks need energy, while energy grids need data to operate efficiently. This project will develop a framework that will optimise the interplay between energy grids and telecommunications and data networks in a way that both the infrastructure pillars (energy and telecommunications) are jointly sustainable and efficient.
Through the Staff Exchange program, we will be able to exchange expertise and know-how between energy, data and telecommunications sectors across both academia and industry.
Use Case Scenarios
a) To investigate optimisation algorithms for energy efficiency under simultaneous wireless information and power transfer (SWIPT) in a local energy system context for a wireless sensor network.
b) To develop a novel framework for predicting and validating trading optimisation strategies for in-house energy asset management, considering battery storage, flexible domestic demand, wind-farm, solar cells etc,. using neural network and transfer learning-based models; while maintaining sustainable and secure exchange of data and user (or individual residence) portfolio.
c) To design new measurement methods and open data sets for analyzing 5G/6G RAN energy consumption, including parametric and generative neural network models for the energy transfer function in both uplink and downlink.
d) To formulate joint data-energy-transportation robust/stochastic optimisation algorithms considering computational load flexibility, intermittent energy generation and storage and multi-agent learning algorithms for collaborative e-transportation and SLES.