S
Siyue Guo
Researcher at Tsinghua University
Publications - 26
Citations - 1054
Siyue Guo is an academic researcher from Tsinghua University. The author has contributed to research in topics: China & Energy consumption. The author has an hindex of 9, co-authored 20 publications receiving 510 citations.
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Roadmap towards clean and low carbon heating to 2035: A provincial analysis in northern China
TL;DR: Wang et al. as discussed by the authors studied the potential contribution to address climate change and the difference of provinces and urban-rural areas by 2035 with the considerations of both air pollutants and carbon emissions reduction using the China Regional Energy System Model.
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Global comparison of building energy use data within the context of climate change
TL;DR: In this article, the authors compared the energy use intensity, energy structure, and carbon emissions in different countries using selected indicators and different projections were fitted presenting different development patterns, and a new clustering of these countries is then proposed.
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A novel approach for selecting typical hot-year (THY) weather data
TL;DR: Wang et al. as discussed by the authors analyzed the indoor thermal environment using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events.
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The typical hot year and typical cold year for modeling extreme events impacts on indoor environment: A generation method and case study
Siyue Guo,Da Yan,Chenxi Gui +2 more
TL;DR: In this article, the indoor environment was simulated in a typical residential building in China's hot summer and cold winter climate zone, that has faced both kinds of events, using ERA5 database (reanalysis weather data) in the past 40 years.
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Clustering-based probability distribution model for monthly residential building electricity consumption analysis
TL;DR: Wang et al. as discussed by the authors proposed a clustering-based probability distribution model to simulate and generate electricity use curves in residential buildings with a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns.