In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using Artificial Intelligence (AI) techniques jointly with linear programming based multiobjective optimization. The proposed Multiobjective Intelligent Energy Management (MIEM) aims to minimize the operation cost and the environmental impact of a microgrid taking into account its pre-operational variables as future availability of renewable energies and load demand. An artificial Neural Network Ensemble (NNE) is developed to predict 24 hour ahead photovoltaic generation, one hour ahead wind power generation and load demand. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this study, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results shows considerable minimization on operation cost and emission level comparing to literature microgrid energy management approaches based on opportunity charging and heuristic flowchart battery management. Index Terms-MultiobjectiveIntelligent Energy Management (MIEM), Neural Network Ensemble (NNE), Fuzzy Logic, Shortterm Forecasting, Microgrid.