The composite load model is one of the most comprehensive and widely used load models, as it includes and differentiates between static and dynamic load components. The simulation results, in which various load models were used, showed that the use of this model provides a good agreement between the simulated and measured responses. In order to obtain information about the composition of the load for the day ahead, a simple but improved artificial neural network (ANN) was used. It requires forecast active and reactive load data and gives as output the participation of each component of the composite load model. Forecast values of total active and reactive demand were obtained using another ANN which has the same settings as the one for load decomposition, but with different input and target. To show how much the forecast values of active and reactive demand affect the accuracy of the forecasted components of the composite load model, a load decomposition forecast was made for 7 days. The results showed that the forecast values of the total active and reactive demand do not proportionally affect the load decomposition error and depend on the variability of daily consumption and the use of the most recent historical data.

Efficient work of grids with the maximum potential utilization, together with supply and modern consumer satisfaction, as well as unpredictability of distributed energy sources, represents the challenge of successful load management. This paper proposes the load management framework and analyses state-of-the-art research activities from the area of load management in smart grids. It addresses three steps in load management framework: modelling and prediction, measurement and monitoring, and optimization and control. The original contribution of this meta-analysis and state-of-the-art review is a multi-factor approach to the process of load management with the identification of key influence factors from groups: system, context and user.