SPATIAL-TEMPORAL CONTROLS ON COOLING DEGREE HOURS - AN ENERGY DEMAND PARAMETER

被引:5
|
作者
BRAZEL, AJ [1 ]
VERVILLE, HJ [1 ]
LOUGEAY, R [1 ]
机构
[1] SUNY COLL GENESEO, DEPT GEOG, GENESEO, NY 14454 USA
关键词
D O I
10.1007/BF00866183
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Present-day and 2 x CO2 regional climate impacts and the effects of local land use patterns on Cooling Degree Hours (CDHs - an energy demand parameter based on cumulative degrees of temperature above 75-degrees-F) are investigated for the Phoenix metropolitan area in central Arizona. Approaches include: (1) the utilization of four readily available and commonly used Global Circulation Models (GCMs) to assess possible changes in climate for doubled CO2, (2) the analysis of hourly temperature data collected for one year over three different land type sites, and (3) analysis of locally collected hourly temperature data, for a typical summer day from a real-time weather and climate network, to evaluate the spatial variability of CDHs over the urban landscape. Results are discussed by showing effects at the global and urban scales. Differing surface types, and expected changes in land uses in the future, induce spatial differences of CDHs (and therefore potential energy demand) comparable to GCM projections of climate change for the region.
引用
收藏
页码:81 / 92
页数:12
相关论文
共 50 条
  • [31] Spatial-Temporal Diffusion Convolutional Network: A Novel Framework for Taxi Demand Forecasting
    Luo, Aling
    Shangguan, Boyi
    Yang, Can
    Gao, Fan
    Fang, Zhe
    Yu, Dayu
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 11 (03)
  • [32] Demand Driven Store Site Selection via Multiple Spatial-temporal Data
    Xu, Mengwen
    Wang, Tianyi
    Wu, Zhengwei
    Zhou, Jingbo
    Li, Jian
    Wu, Haishan
    [J]. 24TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2016), 2016,
  • [33] BERT-Based Deep Spatial-Temporal Network for Taxi Demand Prediction
    Cao, Dun
    Zeng, Kai
    Wang, Jin
    Sharma, Pradip Kumar
    Ma, Xiaomin
    Liu, Yonghe
    Zhou, Siyuan
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) : 9442 - 9454
  • [34] TAXI ORIGIN-DESTINATION DEMAND PREDICTION WITH CONTEXTUALIZED SPATIAL-TEMPORAL NETWORK
    Qiu, Zhilin
    Liu, Lingbo
    Li, Guanbin
    Wang, Qing
    Xiao, Nong
    Lin, Liang
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2019, : 760 - 765
  • [35] A spatial-temporal decomposition approach to performance assessment in energy and emissions
    Ang, B. W.
    Su, Bin
    Wang, H.
    [J]. ENERGY ECONOMICS, 2016, 60 : 112 - 121
  • [36] Spatial-temporal pattern in the cooling effect of a large urban forest and the factors driving it
    Yin, Shi
    Peng, Lilliana L.H.
    Feng, Ningye
    Wen, Hui
    Ling, Ziyao
    Yang, Xiaoshan
    Dong, Lina
    [J]. Building and Environment, 2022, 209
  • [37] Characteristics and spatial-temporal patterns of supply and demand of ecosystem services in the Taihang Mountains
    Qi, Fei
    Liu, Jintong
    Gao, Hui
    Fu, Tonggang
    Wang, Feng
    [J]. ECOLOGICAL INDICATORS, 2023, 147
  • [38] A Multi-View Spatial-Temporal Network for Vehicle Refueling Demand Inference
    Ma, Bo
    Yang, Yating
    Zhang, Guangyi
    Zhao, Fan
    Wang, Yi
    [J]. KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2019, PT II, 2019, 11776 : 412 - 423
  • [39] Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
    Yao, Huaxiu
    Wu, Fei
    Ke, Jintao
    Tang, Xianfeng
    Jia, Yitian
    Lu, Siyu
    Gong, Pinghua
    Ye, Jieping
    Li, Zhenhui
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 2588 - 2595
  • [40] Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
    Liu, Lingbo
    Qiu, Zhilin
    Li, Guanbin
    Wang, Qing
    Ouyang, Wanli
    Lin, Liang
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (10) : 3875 - 3887