Affiliation:
1. UCL Energy Institute, UK
2. EIFER European Institute for Energy Research, Germany
Abstract
This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.
Reference35 articles.
1. Distributed energy generation and sustainable development
2. Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks
3. Bahu, J.-M., Koch, A., Kremers, E., & Murshed, S. M. (2013). Towards a spatial urban energy modelling approach. In Proceedings of 3D GeoInfo Conference (pp. 27-29).
4. Barrett, M., & Spataru, C. (2011). DYNEMO: Dynamic Energy Model, Model Documentation. Retrieved from http://www.ucl.ac.uk/energymodels/models/dynemo
5. Barrett, M., & Spataru, C. (2012). DEAM: Dynamic Energy Agents Model. Retrieved from http://www.ucl.ac.uk/energy-models/models/deam
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献