Data Analytics for Large-Scale Building Energy Modelling and Optimization

Dear Colleagues,

Building energy modeling is transforming from the individual building scale to large-scale district- or urban-level modeling. Traditional modeling methods need to be updated and extended in order to capture interactions and macro-level patterns. Due to the variations in city size, urban configurations, and weather conditions, energy modeling has to be customized according to local context.

Data analytics—a process of modelling, summarizing, and interpreting quantitative outcomes—has been considered one of the viable approaches to generate synthetic data, predict energy demand, provide global control signals, extract spatio-temporal interactions, and test automation algorithms. Along with the evolution of sensor techniques and digitalization, future data analytics should comprise collaborating fields for building energy modeling such as artificial intelligence, decision-support systems, resource allocation, experimental design, geographical information systems, and statistical inference. In the future, an integrated platform of data acquisition, analytics, and management for multiple building is envisioned to help stakeholders monitor the digitized information.

There will be many uncertain and challenging factors in the future, such as climate change and population growth, that affect the modeling methods. Advanced methods handling complex data are also expected to address these challenges. With the increase of modelled building scales, new collaborative controls among energy systems are also becoming popular for performance improvements at the community, district, or urban level. Due to the complexity of computation, advanced data analytic techniques and intelligent algorithms are often needed to solve the optimization problems. Topics addressing controls at these levels are also welcome.

The aim of this Topical Collection is to give an opportunity to scientists investigating building energy modelling and optimization at a large scale to publish their works and make large-scale building energy modelling more accurate and feasible.

Dr. Mengjie Han
Dr. Pei Huang
Prof. Dr. Xingxing Zhang
Collection Editors

Manuscript Submission Information

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