{"title":"结合地理信息和气候数据开发台湾台中市城市建筑能源预测模型","authors":"Cing Chang, Chieh-Yu Chen, Tzu-Ping Lin","doi":"10.1016/j.scs.2024.105949","DOIUrl":null,"url":null,"abstract":"<div><div>Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R<sup>2</sup> value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000 °C-h and with an annual energy consumption of 44–64 kWh/m<sup>2</sup>. Conversely, rural areas have a lower cooling demand – that is, below 8,000 °C-h, with an annual energy consumption of <10 kWh/m<sup>2</sup>.</div><div>Considering the IPCC's RCP8.5 warming scenario, October shows a 20–40 % increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105949"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan\",\"authors\":\"Cing Chang, Chieh-Yu Chen, Tzu-Ping Lin\",\"doi\":\"10.1016/j.scs.2024.105949\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R<sup>2</sup> value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000 °C-h and with an annual energy consumption of 44–64 kWh/m<sup>2</sup>. Conversely, rural areas have a lower cooling demand – that is, below 8,000 °C-h, with an annual energy consumption of <10 kWh/m<sup>2</sup>.</div><div>Considering the IPCC's RCP8.5 warming scenario, October shows a 20–40 % increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"117 \",\"pages\":\"Article 105949\"},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221067072400773X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221067072400773X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan
Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000 °C-h and with an annual energy consumption of 44–64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000 °C-h, with an annual energy consumption of <10 kWh/m2.
Considering the IPCC's RCP8.5 warming scenario, October shows a 20–40 % increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;