Electric grid power consumption load is one of the fundamental areas that need to be faced to provide a sustainable and green ecosystem in smart cities. Consumption load as well as supply and availability of electricity to suppliers and customers is a major issue to be faced to have a balanced smart city power grid infrastructure. Balancing in this case is assumed as a well-designed supply chain management system to be applied in the Smart City (SC) of Athens, Greece. Core of such a system is the knowledge of electric power consumption load per weekly basis of a year, that is the granularity of the proposed system is one week of the system’s operation. In this paper, focus is given on the electric load forecast component of an Energy Management System (EMS) such as the Independent Power Transmission Operator (ITPO) of Greece. Concretely, stochastic data of electric energy consumption load are used to predict the demand or offering of electric power in the future. This is achieved by incorporating a machine learning second-order exponential smoothing algorithm. Such an algorithm is able to speculate near or far in the future power consumption load thus providing a promising parameter to predict smart city needs for electric power in the future. Adopted system is evaluated by the evaluation metric of Normalized Root Mean Square Error (NRMSE), which assures that the system can be used for future predictions of electric power consumption load in smart cities.