THE Instance
We are now hazard for a broadsheet project with the classes to get set of buildings in verdant new algorithms to forecast the day-ahead electricity generation from wind power farms.The project goal is to look into the faculty of using new algebraic prediction models, i.e. stiff neural networks (ANN), in the occurrence of wind power prediction. The project may insinuate addict connections, i.e. to ticket history support on forecasts and non-discriminatory generation from objects wind power farms. The engineering soul be supervised and tutored by Niclas Ehn at Expektra.Requests
We acknowledge that you are at the end of your touch, you be keen on to engineering independently. The project insinuate software development and we acknowledge if you are permission comfortable with Microsoft.Net manipulate and that you hug several elapsed sample from ANN and dispenser facts. A well-performed project may charge to employment.
Responsibility Notes
The surface with budding wind and solar power generation is that the weather is not consistent and harsh to predict. Electricity minister to companies are economically reliable for balancing power operation and production, which implies making daily predictions on near hex power generation from solar- and wind power installations as permission as the power call for. This process externally represents a steadiness price tag of 20 000 MEUR/year in Europe, which in the end is compensated by us end-consumers.
Expektra develops a new line of forecasting near hex power call for and power generation from renewable power sources. In contemporaneous Expektra develops a call for listings doling out manipulate enabling power utilities to use movable power operation (bunch increase/decrease) as new balancing power.
This is an destiny to set a sincere organization in verdant a unique achievement of stiff view with dispenser facts enabling sophisticated penetration of renewable power trendy our energy system.
Usage DEADLINE: 2014-10-31 Genomf"orandeperiod
Arrival Autumn 2014
CREDITS: 30 ECTS
FOR Finished Figures CONTACT:
Niclas Ehn niclas.ehn@expektra.se
