NOWCASTING: Enhancing wind power forecasts using live observational data

The intern will develop and evaluate new algorithms to improve the accuracy of short-term wind power forecast. The algorithm will be fed with near real-time data (wind speed, wind direction, air temperature, power production, turbine availability) from wind farms in order to improve the forecast over the next 24h. Once the best algorithm has been selected, the intern will then apply this new algorithm directly into WPreds IT infrastructure and will train WPreds scientific staff to use the algorithm. With this project, WPred will position itself as a leader in short-term wind power forecast in Canada and on the international stage, allowing its clients (wind farms, grid operators, maintenance groups) to better optimize their respective operations and increase the profitability of the wind power industry.

Faculty Supervisor:

Roland Malhame

Student:

Olivia Beauregard-Harvey

Partner:

Wpred

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

Program: