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Personal profile

Personal Statement

The growth of wind power requires improvements in short-term wind forecast at wind energy sites. Accurate wind speed prediction is desirable where forecasting for available power from wind turbines may allow to schedule for the required back-up by power generation from traditional or other renewable energy sources. Moreover, for power system operators and trading on the energy market is of fundamental importance to have accurate wind farms’ power output.

This project involves spatio-temporal predictions of wind speed and direction by means of linear complex valued prediction filters. The aim will be to investigate several aspects of the algorithm in order to accurately predict the wind speed. Suitable data will be explored and several applications could be investigated. A first and important application is to accurately predict the wind speed at a number of wind farm sites a few hours ahead, for this purpose data from a real wind farm might be available. As a second application, a low-power renewable-energy communications base station network on the Western Isles of Scotland could be explored.

Academic / Professional qualifications

Degree: MSc Physics (Atmospheric Physics), University of Bologna;          

BSc Physics, University of Pisa - Italy

Professional Membership: IET

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy


  • Short-term wind forecast
  • spatio-temporal prediction
  • multichannel adaptive filter
  • signal-processing
  • Wind Energy
  • Wind forecast


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