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

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...

Full description

Bibliographic Details
Main Authors: Taeyoung Kim, Jinho Kim
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/14/14/4256
id doaj-c8fafd21f21f4ac0aa352087c8690be3
record_format Article
spelling doaj-c8fafd21f21f4ac0aa352087c8690be32021-07-23T13:39:02ZengMDPI AGEnergies1996-10732021-07-01144256425610.3390/en14144256A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic InstallationTaeyoung Kim0Jinho Kim1Gwangju Institute of Science and Technology (GIST), School of Integrated Technology, Gwangju 61005, KoreaGwangju Institute of Science and Technology (GIST), School of Integrated Technology, Gwangju 61005, KoreaRooftop 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.https://www.mdpi.com/1996-1073/14/14/4256regional PV output forecastingupscaling methodrooftop PVunauthorized PV installation
collection DOAJ
language English
format Article
sources DOAJ
author Taeyoung Kim
Jinho Kim
spellingShingle Taeyoung Kim
Jinho Kim
A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
Energies
regional PV output forecasting
upscaling method
rooftop PV
unauthorized PV installation
author_facet Taeyoung Kim
Jinho Kim
author_sort Taeyoung Kim
title A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
title_short A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
title_full A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
title_fullStr A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
title_full_unstemmed A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
title_sort regional day-ahead rooftop photovoltaic generation forecasting model considering unauthorized photovoltaic installation
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-07-01
description 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.
topic regional PV output forecasting
upscaling method
rooftop PV
unauthorized PV installation
url https://www.mdpi.com/1996-1073/14/14/4256
work_keys_str_mv AT taeyoungkim aregionaldayaheadrooftopphotovoltaicgenerationforecastingmodelconsideringunauthorizedphotovoltaicinstallation
AT jinhokim aregionaldayaheadrooftopphotovoltaicgenerationforecastingmodelconsideringunauthorizedphotovoltaicinstallation
AT taeyoungkim regionaldayaheadrooftopphotovoltaicgenerationforecastingmodelconsideringunauthorizedphotovoltaicinstallation
AT jinhokim regionaldayaheadrooftopphotovoltaicgenerationforecastingmodelconsideringunauthorizedphotovoltaicinstallation
_version_ 1721288612486578176