SEERI (Solar Energy Estimator for Rooftop in Indonesia)

Indonesia has set itself the target of achieving a 23 percent share of renewable energy in the national energy mix by 2025. The country is focussing on the introduction of renewable energy adoption and is developing rooftop photovoltaic (PV) systems. To promote the use of rooftop PV systems, a pilot project called SEERI (Solar Energy Estimator for Rooftop in Indonesia) has been initiated through a collaboration between the Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ), GSMA, and Bappenas, the key partner of the Digital Transformation Center Indonesia.

SEERI uses the power of digital technology by combining satellite imagery and Artificial Intelligence (AI) to automatically identify roof span detection. It evaluates the potential for photovoltaic solar systems on rooftops in specific areas. The Bali Province has been chosen as the pilot region for SEERI’s implementation, with full support from the Bali Provincial Government and the Center for Community-based Renewable Energy (CORE) LPPM Udayana University.

This project has the potential to bring significant economic and environmental benefits for Indonesia. Important recommendations and considerations are necessary to ensure the successful implementation of SEERI:

• Relevance and Scalable Solution: SEERI addresses the growing demand for renewable energy in Indonesia and has the potential to transform the rooftop PV sector. This could have a significant impact on the nation’s renewable energy goals.
• Partners for Solution Design: Collaboration with various stakeholders is essential for SEERI’s success. In addition to existing partners, further engagement with academic institutions can increase the reach of the project.
• Stakeholders: The key stakeholders include the Indonesian government, the public, and potential users of rooftop PV systems. These stakeholders benefit from SEERI’s accurate assessments and economic advantages.
• Resources: A comprehensive dataset is needed to increase the effectiveness of SEERI and to train the detection model. While SEERI already possesses a dataset model for buildings in Bali, additional data is needed to account for regional building variations. Access to high-resolution satellite imagery is recommended for optimal training data and model accuracy.
• Expertise: To ensure the accuracy of SEERI’s calculation reports, an understanding of the local PV Rooftop market and regulations is essential. Not only expertise in AI and Machine Learning (ML) related to the development of the system, but also expertise understanding irradiation data in the region, local regulations, and component pricing is mandatory.
• First Steps: The implementation includes obtaining additional datasets to train the detection model, working with local experts on PV Rooftop regulations and market dynamics, considering the use of high-resolution satellite images. Collaboration with service providers or PV providers is also recommended, as SEERI currently provides potential assessment reports, and professional analysis of rooftops or assets is needed for further project phases.

SEERI represents a ground-breaking opportunity to advance renewable energy in Indonesia and can serve as a model for other regions facing similar energy transition challenges. By following these recommendations and engaging with the right partners, SEERI can have a lasting impact on Indonesia’s renewable energy landscape.