Farzin Golzar

Farzin Golzar works as Assistant Professor at KTH, the Heat and Power Technology Division. He focuses on addressing multi-disciplinary renewable energy issues by implementing innovative methods, including hybrid Data-Driven Statistical Models and Process Driven Physical Models. Moreover, Farzin integrates the Life Cycle Assessment (LCA) approach with techno-economic analysis of renewable energy technologies to assess and compare various systems' environmental impacts comprehensively.

Before joining the division, Farzin worked as a climate and energy strategy advisor with Swedish and international clients and customers from different sectors, including buildings, transportation, agriculture, etc. Farzin has post-doctoral experience at the Energy System Division at KTH. In collaboration with KTH water center, he worked on ensuring sustainability and equality of water and energy systems during actor-driven disruptive innovation.

Farzin holds a Ph.D. in Energy Systems Engineering from Sharif University of Technology, focusing on the Techno-Economic and Environmental optimization of Energy Supply Systems (2019). Farzin carried out the environmental part of his Ph.D. at ETH Zurich in collaboration with Chair of Ecological Systems Design (2017). He also holds an MSc degree in the field of optimal design of high-temperature fuel cells for stationary applications (2014) as well as a BSc degree in the area of Phase Change Materials (PCM) as a Chemical Engineer (2012).

Some of ongoing research project Farzin is leading are:

• A turnkey solution for Swedish buildings through integrated PV electricity and energy storage (PV-ESS)
• A new standard methodology for assessing the environmental impact of stationary energy storage systems (LCA-SESS)
• Digital Twin for smart grid-connected buildings
• PED StepWise — Participatory Step-by-Step Implementation Process for Zero Carbon District Concepts in Existing Neighbourhoods

Farzin’s research interests include:

• Application of Artificial Intelligence (AI) in modeling and optimizing renewable energy resources

• Developing hybrid Data-Driven Statistical Models and Process Driven Physical Models

• Comparative Life Cycle Impact Assessment (LCIA)

• Energy conversion and storage technologies