Electricity consumption of Singaporean households reveals proactive community response to COVID-19 progression

Understanding how populations’ daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity-consumption data to unravel behavioral changes within peoples’ homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents—who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates—a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic.

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Electricity Consumption of Singaporean Households Reveals Proactive Citizen Response to COVID-19 Progression

10.21203/rs.3.rs-130437/v1 ◽

2020 ◽

Author(s):

Gururaghav Raman ◽

Jimmy Chih-Hsien Peng

Keyword(s):

Influencing Factor ◽

Electricity Consumption ◽

Community Response ◽

Strong Correlations ◽

Energy Meter ◽

Residential Electricity ◽

The Government ◽

Residential Electricity Consumption ◽

City State ◽

Citizen Response

Abstract Understanding how populations’ daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity consumption data to unravel behavioral changes within citizens’ homes in this period. Based on smart energy meter data from 10,246 households in Singapore, we find strong correlations between the pandemic progression in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying the arrival of a ‘new normal’ in the residents’ lifestyles. These observations point to a proactive response from Singaporean residents---who increasingly stayed at home during evenings despite not being forced by the government to do so using a lockdown---a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events such as a pandemic.

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The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea

Scientific Data ◽

10.1038/s41597-019-0212-5 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 5

Author(s):

Changho Shin ◽

Eunjung Lee ◽

Jeongyun Han ◽

Jaeryun Yim ◽

Wonjong Rhee ◽

...

Keyword(s):

Energy Consumption ◽

Sampling Rate ◽

Electricity Consumption ◽

Consumption Pattern ◽

Additional Information ◽

Consumption Data ◽

Energy Disaggregation ◽

Residential Electricity ◽

Residential Electricity Consumption

Abstract AMI has been gradually replacing conventional meters because newer models can acquire more informative energy consumption data. The additional information has enabled significant advances in many fields, including energy disaggregation, energy consumption pattern analysis and prediction, demand response, and user segmentation. However, the quality of AMI data varies significantly across publicly available datasets, and low sampling rates and numbers of houses monitored seriously limit practical analyses. To address these challenges, we herein present the ENERTALK dataset, which contains both aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. Among the publicly available datasets with both aggregate and per-appliance measurements, 15 Hz was the highest sampling rate. The number of houses (22) was the second-largest where the largest one had a sampling rate of 1 Hz. The ENERTALK dataset is also the first Korean open dataset on residential electricity consumption.

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Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa

South African Computer Journal ◽

10.18489/sacj.v32i2.845 ◽

2020 ◽

Vol 32 (2) ◽

Author(s):

Wiebke Toussaint ◽

Deshendran Moodley

Keyword(s):

South Africa ◽

Domain Knowledge ◽

Expert Knowledge ◽

External Validation ◽

Electricity Consumption ◽

Consumption Data ◽

Residential Electricity ◽

Energy Domain ◽

Term Energy ◽

Residential Electricity Consumption

Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters. Based on an application case study in South Africa, we present an approach for formalising implicit expert knowledge as external evaluation measures to create customer archetypes that capture variability in residential electricity consumption behaviour. By combining internal and external validation measures in a structured manner, we were able to evaluate clustering structures based on the utility they present for our application. We validate the selected clusters in a use case where we successfully reconstruct customer archetypes previously developed by experts. Our approach shows promise for transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.

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