Analyzing Energy Usage on a City-scale using Utility Smart Meters
Analyzing Energy Usage on a City-scale using Utility Smart Meters
复制标题
使用公用事业智能电表分析城市规模的能源使用情况
DOI：
10.1145/2993422.2993425
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Prashant J. Shenoy
中科院分区：
文献类型：
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作者：
Srinivasan Iyengar;Stephen Lee;David E. Irwin;Prashant J. Shenoy
了解建筑物的能源使用情况对于政策制定、能源规划和实现可持续发展至关重要。不幸的是，测量建筑物以收集能源使用数据是困难的，所有公开可用的数据集通常只包括一个地区内的几百个家庭。由于其相对较小的尺寸，这些数据集提供有限的洞察力，并且不足以用于需要更大表示的分析，例如整个城市或城镇。近年来，公用事业公司已经安装了先进的电表和燃气表，即，“智能电表”，使能源数据收集的大规模。在本文中，我们分析了一家公用事业公司的数据集，其中包括覆盖一个小城市的14，836个智能电表的能源数据。我们对城市的燃气和电力数据进行广泛的分析，以深入了解单个家庭和整个城市的能源消耗情况。在这样做的过程中，我们展示了城市规模的智能电表数据集如何回答有关建筑物能源消耗的各种问题，例如天气对能源使用的影响，建筑物的大小和年龄与其能源使用之间的相关性，可再生能源渗透率提高的影响等。夏热冬冷日分别增加36%和11.5%。再举一个例子，我们观察到700个家庭的能源效率非常低，因为其能源需求变化性是电网总需求的两倍。最后，我们研究了家庭中可再生能源整合水平提高的影响，并表明太阳能渗透率高于需求的20%会增加过度发电的风险，并可能影响公用事业运营。
Understanding the energy usage of buildings is crucial for policy-making, energy planning, and achieving sustainable development. Unfortunately, instrumenting buildings to collect energy usage data is difficult and all publicly available datasets typically include only a few hundred homes within a region. Due to their relatively small size, these datasets provide limited insight and are insufficient for analyses that require a larger representation, such as an entire city or town. In recent years, utility companies have installed advanced electric and gas meters, i.e., "smart meters" that enable energy data collection on a massive scale. In this paper, we analyze such a dataset from a utility company that includes energy data from 14,836 smart meters covering a small city. We conduct a wide-ranging analysis of the city's gas and electric data to gain insights into the energy consumption of both individual homes and the city as a whole. In doing so, we demonstrate how city-scale smart meter datasets can answer a variety of questions on building energy consumption, such as the impact of weather on energy usage, the correlation between the size and age of a building and its energy usage, the impact of increasing levels of renewable penetration, etc. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we observe that 700 homes are highly energy inefficient as its energy demand variability is twice that of the aggregate grid demand. Finally, we study the impact of increasing level of renewable integration in homes and show that solar penetration rates higher than 20% of demand increases the risk of over-generation and may impact utility operations.