General demand response resources

such as distributed generators

electric vehicles and energy storage

which increasingly access to power consumer side of power distribution systems and take demand response under the incentive of the power market

will change the load characteristics and load profile and affect power load forecasting. Based on predictability of the demand response planning signals and independence of seasonal basis load

the historical load data of active distribution network is decomposed into the seasonal basis load and the other load associated with demand response signals and meteorological factors by applying the wavelet decomposition method. The time series forecasting model is applied to forecast seasonal basis load

and support vector regression model is applied to forecast the related load with demand response and meteorological factors. Then

the total load can be obtained with the combination forecasting model. The load data of active distribution network resulted from simulation of linear time-varying model is used to forecast load profile with different load forecasting methods

and the example results demonstrate that the presented load forecasting method is more effective and accurate for load forecasting of active distribution network including demand response.