Monthly Net Electricity Consumption Prediction Under High Penetration of Distributed Photovoltaic System

Net electricity consumption (NEC) is the result of the joint action of actual electricity consumption (AEC) and distributed photovoltaic (DPV) generation. The accuracy of NEC prediction affects the retailers' gaming and ultimate interests in electricity market. The ascending DPV installations present new challenges, with the modifications to the NEC curve becoming greater as DPV penetration increases. To track the changes in DPV penetration and improve the prediction accuracy under high penetration of DPV, a monthly NEC prediction model assembled by support vector regression and time series modeling under an online update framework is proposed. First, the DPV features are extracted from a few known solar customers' information to identify whether other customers install DPV or not. Second, an online update framework is proposed and its accuracy is verified by two validations regarding the conversion of non-solar customers to solar customers (namely the change of DPV penetration). Third, a NEC decoupling model based on historical NEC data of months without DPV installation is established. Finally, a monthly NEC prediction model under different DPV penetrations is proposed. Simulation results show that the proposed prediction method with an online update is more accurate than the individual time series model, and the performance of the prediction model is getting better with the increasing DPV penetration.