Dr Giuseppe Colantuono
Senior Research Fellow
Giuseppe wrote and leads the Interreg NWE project "RED WoLF" (Rethink Electricity Distribution Without Load Following), worth €6.06m, and the subsequently awarded RED WoLF capitalisation initiative, worth €1.28m.
RED WoLF reduces CO2 emission from NWE homes by storing low CO2 energy generated by on-site PV (the easy part) and available on the Electric Grid during favorable time intervals (which requires planning and computations). Prediction algorithms estimate the electricity needed in the houses within the next two days, and plan intake and storage of the greenest/cheapest electricity available. Direct thermal storage enables reduction of batteries' size to few kWh while still having the capacity of store the energy needed in a home for the next day or more. This makes the electricity demand of residential buildings adaptable to the energy availability on the Grid: i.e., taking electricity when is abundant rather than when is in high demand and/or short supply.
Giuseppe also leads the AIDAC-19 project, based on internal School's funding awarded on a competitive basis. The aim is to produce a risk function able to link the indicators of global spread of Covid-19 (and other infectious diseases) to the detection of Covid-19 from sewage water in buildings and to an automated air disinfection system.
Giuseppe's research group includes two Postdoctroal Research Fellows, Dr Alexander Shukhobodskiy and Dr Aleksandr Zaitcev, employed full-time on RED WoLF, and a PhD student, Mr Geert Verhoeven, exploring the present and future interplay between renewable energy and commuting patterns of electric vehicles.
Giuseppe is the leader of the "Maths for Data Scientists" module in the BSc (Hons) Data Science course at Leeds Beckett.
- Maths for Data Scientists
Giuseppe's current research activity is centred on modelling the interaction between environmental conditions and electricity generation and demand. He is also active in modelling disease detection and transmission. His research over the years spans from geophysical fluid dynamics to applied statistical methods, applied optics and artificial intelligence.
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- Renewable Energy