With the emergence of the smart grid, the customers get the chance to involve in demand-side management and to combine renewable energy sources. Renewable energy sources integration in smart grid is difficult due to intermittent and fluctuating nature of Renewable energy. The variation in renewable energy generation is smooth out by utilizing the Energy Storage System in a smart house. This paper introduces the Smart Home Energy Management System using Multi-output Adaptive neuro-fuzzy inference system for efficient management of Energy Storage System, scheduled appliances and to integrate Renewable energy. The controller can interrupt or postpone scheduled appliances and trading surplus energy between consumers thereby reducing reverse power flow and electricity cost. The proposed system is tested with daily data of wind speed, temperature, insolation, uncontrollable and controllable appliances power and electrical energy price as inputs to validate the results. The output of the control system decides how to handle energy production, consumption, and scheduling of the appliance. The result shows that the electricity cost reduced by 57.62%, peak power consumption is reduced by 44.4% and peak-to-average ratio is reduced by 73.6% after adopting the proposed strategy.