摘要
•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.