Introducing a new multi-stage SEMS architecture for optimal energy management in MGs considering various resources of uncertainties.

Performing various tasks such as data acquisition/mining/refinement, pattern recognition, learning parameters and offline/online decision making.

Some data mining algorithms have been applied to reduce the huge amount of raw data, recognize patterns for analysis and learn the given parameters.

For handling of uncertainties, using a stochastic scheduling approach, which includes the mean and variance of energy cost, is applied in the optimization process.