The role of household level electricity data in improving estimates of the impacts of climate on building electricity use

Chen, Mo; Ban-Weiss, George; Sanders, Kelly T.

doi:10.1016/j.enbuild.2018.09.012

Cited by 30 publications

(22 citation statements)

References 42 publications

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“…In this model, a stationary point is located by iteration to achieve the overall best two-piece linear fit to the dataset. Our previous study (Chen et al 2018) shows that using daily accumulated electricity consumption data and daily mean temperature yields the best model performance. Hence, we aggregated hourly electricity data records to daily accumulated electricity consumption (kWh day −1 ) for analysis.…”

Section: Statistical Modelmentioning

confidence: 99%

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A new method utilizing smart meter data for identifying the existence of air conditioning in residential homes

Chen

Sanders

Ban-Weiss

2019

Environ. Res. Lett.

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Climate change, urbanization, and economic growth are expected to drive increases in the installation of new air conditioners, as well as increases in utilization of existing air conditioning (AC) units, in the coming decades. This growth will provide challenges for a diversity of stakeholders, from grid operators charged with maintaining a reliable and cost-effective power system, to low-income communities that may struggle to afford increased electricity costs. Despite the importance of building a quantitative understanding of trends in existing and future AC usage, methods to estimate AC penetration with high spatial and temporal resolution are lacking. In this study we develop a new classification method to characterize AC penetration patterns with unprecedented spatiotemporal resolution (i.e. at the census tract level), using the Greater Los Angeles Area as a case study. The method utilizes smart meter data records from 180 476 households over two years, along with local ambient temperature records. When spatially aggregated, the overall AC penetration rate of the Greater Los Angeles Area is 69%, which is similar to values reported by previous studies. We believe this method can be applied to other regions of the world where household smart meter data are available.

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Section: Statistical Modelmentioning

confidence: 99%

“…To describe the nonlinear relationship between residential electricity consumption and ambient temperature, we implemented the segmented linear regression model described in our previous study (Chen et al 2018). In this model, a stationary point is located by iteration to achieve the overall best two-piece linear fit to the dataset.…”

Section: Statistical Modelmentioning

confidence: 99%

A new method utilizing smart meter data for identifying the existence of air conditioning in residential homes

Chen

Sanders

Ban-Weiss

2019

Environ. Res. Lett.

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“…Developing a quantitative understanding of how residential electricity use by disparate populations will be impacted in the future by factors such as climate change and urban heat islands requires high resolution energy and climate data to (a) identify the geospatial distribution of warming and (b) quantify how electricity consumption changes according to those temperature and climatic variations (Chen et al 2018). To the authors' knowledge, few empirical studies have been performed to understand the functional relationships between electricity usage and ambient temperature at fine spatiotemporal scales.…”

Section: Introductionmentioning

confidence: 99%

“…This research gap is largely due to a lack of publicly available, high-resolution (e.g. household level and hourly) residential electricity data (Chen et al 2018). However, the recent availability of highly resolved electricity use data, collected by smart meters at the household level, enable such analysis.…”

Section: Introductionmentioning

confidence: 99%

“…To answer these questions, we utilize two years (i.e. 2015-2016) of hourly electricity consumption records for 180 476 households in Southern California, as well as local site weather data, to compute household-level relationships between electricity consumption and ambient temperature, using the segmented linear regression model detailed in our previous study (Chen et al 2018). We also assess the prevalence of AC usage across the investigated area at the household level, in the context of climate characteristics, as well as socio-economic demographics, using a methodology presented in our previous study (Chen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Utilizing smart-meter data to project impacts of urban warming on residential electricity use for vulnerable populations in Southern California

Chen

Ban-Weiss

Sanders

2020

Environ. Res. Lett.

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Extreme heat events are increasing in frequency and intensity, challenging electricity infrastructure due to growing cooling demand and posing public health risks to urbanites. In order to minimize risks from increasing extreme heat, it is critical to (a) project increases in electricity use with urban warming, and (b) identify neighborhoods that are most vulnerable due in part to a lack of air conditioning (AC) and inability to afford increased energy. Here, we utilize smart meter data from 180 476 households in Southern California to quantify increases in residential electricity use per degree warming for each census tract. We also compute AC penetration rates, finding that air conditioners are less prevalent in poorer census tracts. Utilizing climate change projections for end of century, we show that 55% and 30% of the census tracts identified as most vulnerable are expected to experience more than 16 and 32 extreme heat days per year, respectively.

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Does economic progress and electricity price induce electricity demand: A new appraisal in context of Tunisia

Talbi

Jebli

Bashir

et al. 2020

Journal of Public Affairs

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This article discusses the residential electricity (RE) demand in Tunisia as a function of household disposable real Gross Domestic Product (GDP), the price of electricity (PE) and the degree of urbanization (U) spanning the period 1980–2018. To do that, the Autoregressive Distributed Lag (ARDL) bound model and Granger causality test are employed to examine the influencing factors of the changes in RE demand, and to discuss the directions of short and long‐run causalities among the variables. The empirical result shows that all variables are integrated of order one, I (1). The Fisher statistics of the Wald test confirms that variables are cointegrated. Long‐run elasticities suggest that, in the long‐run, electricity price and urbanization have a significant and positive impact on RE demand, while real GDP contributes insignificantly to the decrease of RE demand in Tunisia. Granger causality empirics suggests that in short run, there is bidirectional causality between RE demand and electricity price, and between RE demand and urbanization, and a unidirectional causality from real GDP and urbanization to electricity price, and from real GDP to urbanization.

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The role of household level electricity data in improving estimates of the impacts of climate on building electricity use

Cited by 30 publications

References 42 publications

A new method utilizing smart meter data for identifying the existence of air conditioning in residential homes

A new method utilizing smart meter data for identifying the existence of air conditioning in residential homes

Utilizing smart-meter data to project impacts of urban warming on residential electricity use for vulnerable populations in Southern California

Does economic progress and electricity price induce electricity demand: A new appraisal in context of Tunisia

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