Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Abstract

Rooftop Photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers, and thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation, but also PV capacity information is invisible in unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV and capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 7.13% and the normalized mean absolute error (nMAE) by 3.88%. Additionally, the effectiveness of the proposed methodology is demonstrated through upscaling factor analysis and feature correlation analysis. It can be concluded that regional rooftop PV generation forecasting accuracy is improved.