STAR-SOLAR

8th Joint Call: STAR-SOLAR

The proposal aims to apply a socio-technical approach to harness residential solar PV adoption. STAR-SOLAR integrates technical, social, economic, and policy perspectives to accelerate renewable energy transition through household-level solar PV systems, with innovative methods such as gamification and AI-based monitoring.
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Background

The shift towards Net Zero Emission requires rapid deployment of renewable energy, with residential solar PV offering high potential. However, adoption rates remain below expectations due to behavioral, socio-economic, and policy barriers.

Energy systems are not merely technical infrastructures but are shaped by social, environmental, economic, and political dimensions. Addressing all aspects together, rather than sequentially, is essential for a holistic and integrated renewable energy transition.

The project

STAR-SOLAR will:

  • Analyse current public knowledge, attitudes, and perceptions of solar PV.
  • Evaluate residential PV system performance under diverse environmental conditions.
  • Develop sustainable business models for PV adoption.
  • Create innovative awareness strategies through gamification.
  • Develop an AI-based system for real-time monitoring and predictive maintenance of PV systems.

A three-year programme with mixed methods (quantitative surveys, social media analysis, field data collection, strategic business modelling, gamified tools, and AI system development) is planned.

Expected outcomes: deeper understanding of behavioural barriers, enhanced PV system designs, validated business models, and novel public engagement strategies, boosting adoption nationally and internationally.

The science

The project combines engineering, behavioural science, digitalisation, and sustainability research. Key research areas include:

  • Survey and social media analysis of public perception.
  • Empirical testing of PV systems in varying climates.
  • Business model design for scalable residential PV adoption.
  • Game-based educational tools to increase awareness.
  • Development of AI algorithms for predictive maintenance and monitoring.

The team

  • Dr. Yun Prihantina Mulyani (Coordinator), Universitas Gadjah Mada (UGM), Indonesia
  • Dr. Yousra Sidqi, Lucerne University of Applied Sciences and Arts, Switzerland
  • Dr. Vannak Vai, Institute of Technology of Cambodia, Cambodia
  • Prof. Hideaki Ohgaki, Kyoto University, Japan

 

Contact

Dr. Yun Prihantina Mulyani                     E-Mail: yun.prihantina@ugm.ac.id 

Tags
CLEAN, ACCESSIBLE AND SECURE ENERGY SUPPLY