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 due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and 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 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.
Highlights
IntroductionIn order to solve the problem caused by the use of fossil fuels, electricity is produced by renewable energy sources
This study presents a new forecasting method for regional rooftop PV power generation
An unauthorized PV detection model based on multi-layer perceptron (MLP) by trained virtual typical net load pattern (TNLP) and minimum net load pattern (MNLP) is proposed to detect whether rooftop
Summary
In order to solve the problem caused by the use of fossil fuels, electricity is produced by renewable energy sources. According to the IRENA survey, the capacity of renewable energy utilities increased from. Utilities increased from 73 GW to 713 GW [4] Another reason for the increased supply of solar power facilities is the decline in the levelized cost of electricity (LCOE) [5], and renewable energy policies such as the feed-intariff (FiT) and renewable portfolio standard (RPS) [6]. Rooftop PV increased rapidly due to factors such as a decrease in rooftop PV generation costs [7], incentive for roof PV installations, and reduction of house electricity bills [8,9]. The solar power output has the characteristic that the output is determined according to the amount of irradiance and the PV module temperature, which have intermittent characteristics
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