Sapvia launches solar pv data portal for members

The South African Photovoltaic Industry Association (SAPVIA) has launched a new data portal that gives its members access to periodic data about solar installations across the country. The project, in collaboration with GEOTERRAIMAGE (GTI), was launched this week.

GEOTERRAIMAGE leveraged years of expertise in satellite image interpretation and analysis to develop advanced machine learning methods and artificial intelligence to efficiently extract solar PV panel data from satellite images. The extracted solar PV panel data is linked to individual cadastral parcels, delivering detailed insights into solar PV panel area, kWp capacity, solar PV uptake and land use for each parcel.

SAPVIA members will have access to provincial-level datasets, while members of the public will have access to a high-level, summarised dataset.

“Our mission is to empower our members as well as the public sector with data-driven insights, and this portal is a significant step in that direction,” said SAPVIA CEO Dr Rethabile Melamu.

“By giving them access to information such as the Solar Power Analytics Dataset, they will be able to make informed decisions, plan accurately and strategically, and it will assist them with forecasting.”

The platform is hosted online and will provide access to a wealth of data on the current and potential use of solar energy in South Africa, across multiple industries including the private sector, public sector, and financial institutions, amongst others.

Technical Specialist at SAPVIA, De Wet Taljaard is excited about this collaboration.

“Our goal is to break down the data by market segment, covering residential, commercial, industrial, and utility-scale installations. By doing so, we aim to offer a comprehensive view of the solar PV landscape in South Africa. The first release of data includes the period to the end of Q1 of 2023.

Taljaard says this data reveals interesting insights:

• The total installed solar PV capacity as of the end of Q1 2023 was 5.5GW made up of 2.2GW of public procurement, primarily through the renewable energy independent power producer programme (REIPPP), and 3.3GW of private procurement.

• The residential market makes up 11% of the installed capacity, the rest of the SSEG market, systems smaller than 1MVA including residential and commercial and industrial (C&I), make up 33% of the installed capacity. Systems in the range of 1MW to 50MW make up 34% of the installed capacity and utility scale systems, larger than 50MW, make up 32% of the installed capacity.

• From a municipal point of view, the City of Johannesburg is the metro with the highest installed capacity at 586MW, consisting of residential and C&I systems, closely followed by Pixley ka Seme district municipality with an installed capacity of 583MW made up of REIPPP projects, representing just over 10% of the nation’s installed capacity, respectively.

• The City of Tshwane boasts the largest number of residential installations at 22 956, closely followed by the City of Cape Town at 21 342. eThekwini has the largest average residential systems at 10.8kWp per system, followed by the City of Cape town at 7kWp.

This collaboration goes further than just data dissemination. It is about establishing SAPVIA as a trusted authority in the field of solar PV data.

“We are committed to maintaining the highest standards of accuracy and reliability, reinforcing our role as an industry authority and reliable data provider,” said Dr Melamu.

Through this data portal, SAPVIA hopes to showcase the diverse applications of the collected information.

This approach is designed to attract interest from various stakeholders, including investors looking for opportunities in the solar sector, policymakers seeking data-driven insights for informed decisions, researchers conducting groundbreaking studies, and the general public eager to understand the impact of solar PV installations on our environment and economy.

For more information, visit

Contact SAPVIA or GEOTERRAIMAGE for further information on the Solar Power Analytics Dataset

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