Personlig presentation av Jingchun Shen
She is a building researcher, mechanical engineer, WELL AP in Sweden and serves as a subject editor for the editorial board for buildings and a board member of the MDPI group (applied science, sustainability, energies). , and is passionate about: 1) future climate adaptation / resilient building design, 2) building-integrated solar technology, 3) simulation of urban and building clusters, 4) digital twin technology application and 5) indoor health quality.
She participates in course development in energy efficient building (7.5 credits, BY3001), Energy Performance of Building Simulation & Analysis (IDA ICE software, 5 credits, ABY22W), Solar Thermal (7.5 credits, EG 3007), Sustainable Green Building Rating System (2.5 credits, BY3004), BIM in the construction process (5 hp, GBY2GC) and energy projects (7.5 credits, BY2022).
She works in the team for construction technology where she exhibits for groundbreaking research projects and high-performance tools for building design and analysis. She links the creation of these educational and academic offerings with the commitment to constantly improve construction technology to enable students to create sustainable design and new research.
- The reinforcement learning method for occupant behavior in building control : A review, Energy and Built Environment, 2021, Vol. 2, No. 2, 137-148. Artikel : refereegranskat. .
- Influence of Future Climate on Building Performance and the Related Adaptive Solution to New Building Design, Ingår i: Handbook of Climate Change Mitigation and Adaptation, Springer-Verlag New York, 2021. Kapitel av bok. .
- Building Renovation Adapting to Future Climate: A Potential Solution of Phase-Change Material to Building Envelope, Ingår i: Handbook of Climate Change Mitigation and Adaptation, Springer-Verlag New York, 2021. Kapitel av bok. .
- Tailoring future climate information for building energy simulation, Ingår i: Data-driven analytics for sustainable buildings and cities, Springer, 2021. Kapitel av bok. .
- Machine learning and artificial intelligence for digital twin to accelerate sustainability in positive energy districts, Ingår i: Data-driven Analytics for Sustainable Buildings and Cities, Springer, 2021. Kapitel av bok. .