Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector
Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector
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DOI：
10.1016/j.apenergy.2019.05.011
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发表时间：
2019-09-15
期刊：
影响因子：
11.2
通讯作者：
Hawkes, Adam
中科院分区：
文献类型：
--
作者：
Sachs, Julia;Moya, Diego;Hawkes, Adam
气候条件、人口密度、地理位置和住区结构都对一个国家的供暖和制冷需求产生强烈影响，从而对能源使用和温室气体排放产生强烈影响。特别是，供暖或制冷系统的选择受到现有能源分配基础设施的影响，而这种基础设施的成本与需求的空间密度密切相关。因此，为了提高技术评估的准确性，需要更好地估计需求的空间和时间分布。本文提出了一种地理信息系统方法，将每小时NASA MERRA-2全球温度数据集与空间分辨率人口数据和国家能源平衡相结合，以确定全球高分辨率的热量和冷却能量密度图。然后使用k均值聚类为每个国家生成一组能量密度带。最后，推导了代表每个波段的日和季节变化的需求曲线，以捕捉时间变化。与本文一起发布的165个国家的结果数据集旨在集成到一个名为MUSE（模块化能源系统模拟环境）的新综合评估模型中，但可用于任何国家的供热或制冷技术分析。这些需求概况是能源规划的关键投入，因为它们通过可获得数据的每个国家的一致方法描述了需求密度及其波动。
Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment) but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.