Analysis of single- and multi-family residential electricity consumption in a large urban environment: Evidence from Chicago, IL
Analysis of single- and multi-family residential electricity consumption in a large urban environment: Evidence from Chicago, IL
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DOI：
10.1016/j.scs.2022.104250
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发表时间：
2022-11
影响因子：
11.7
通讯作者：
Jorge E. Pesantez;Grace E. Wackerman;A. Stillwell
中科院分区：
文献类型：
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作者：
Jorge E. Pesantez;Grace E. Wackerman;A. Stillwell
自然和人为引起的极端事件会改变城市地区的居民用电需求，并给电网带来压力，不同类型的居民用电消费者表现出不同的消费模式。考虑到单户消费者，居民电力需求已被广泛分析;然而，多户消费模式仍然相对不足。智能电表的部署能够以高时间分辨率识别单户和多户住宅用电模式。使用智能电表数据为更大的芝加哥地区，我们比较电力需求的配置文件报告的智能电表从一个大的和多样化的城市环境中的单户和多户消费者，以更好地了解居民用电模式。我们的研究全面分析了这两种类型的居民消费者的日常电力需求概况，以确定高峰用电时间和幅度。结果表明，两个住宅终端用户的电力需求遵循类似的时间使用模式，和单户用户的多户用户的需求约为每户的两倍。我们还提出了预测模型的电力需求与社会经济数据在邮政编码水平。预测模型结果表明，多元线性回归模型分别解释了单户和多户用户平均每日电力需求的62%和41%。居住者的中位年龄、65岁及以上的百分比、平均通勤时间和高中或高等教育的百分比是单户用户MDE需求的统计学显著预测因素，其中高中或高等教育的百分比具有最高的相对重要性。同样，中位建筑年龄，多户家庭百分比，女性百分比，居住者的中位年龄和平均通勤时间是多户家庭用电量的统计学显著预测因素，居住者的中位年龄具有最高的相对重要性。通过对电力需求进行建模，揭示单户和多户住宅电力需求之间的差异，可以帮助城市规划者和公用事业管理者制定量身定制的需求管理策略。
Natural and human-caused extreme events can alter residential electricity demand in urban areas and stress the electricity grid, with different types of residential electricity consumers exhibiting different consumption patterns. Residential electricity demands have been widely analyzed considering single-family consumers; however, multi-family consumption patterns remain comparatively understudied. The deployment of smart electricity meters enables the identification of single- and multi-family residential electricity consumption patterns at high temporal resolution. Using smart electricity meter data for the greater Chicago area, we compare electricity demand profiles reported by smart meters from single- and multi-family consumers in a large and diverse urban environment to understand residential electricity patterns better. Our study comprehensively analyzes the daily electricity demand profiles of these two types of residential consumers to identify peak electricity consumption times and magnitudes. Results show that the electricity demand of both residential end-users follows similar time of use patterns, and single-family users approximately double the demand of multi-family users on a per household basis. We also present predictive models of the electricity demand with socioeconomic data at the zip code level. Predictive model results show that multiple linear regression models explain up to 62% and 41% of the mean daily electricity (MDE) demand of single- and multi-family users, respectively. The median age of occupants, percent age 65 and older, mean commute time, and percent high school or higher education are statistically significant predictors of the MDE demand of single-family users, with percent high school or higher education having the highest relative importance. Similarly, median building age, percent multi-family, percent female, median age of occupants, and mean commute time are statistically significant predictors of multi-family electricity consumption, with median age of occupants having the highest relative importance. Modeling electricity demand to uncover differences between single- and multi-family residential electricity demands can assist city planners and utility managers to develop tailored demand management strategies.