Demand baseline estimation using similarity‐based technique for tropical and wet climates

Raman, G. Venkat; Kong, Yaonan; Peng, Jimmy Chih-Hsien; Ye, Zhi‐Sheng

doi:10.1049/iet-gtd.2017.1933

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…By tracking how the households’ electricity demands change during this period, we ascertain links between their behaviors and publicly available information about the progression of the pandemic. Our study shows that a strong positive correlation exists between the household peak consumption and new reported COVID-19 cases and that there is a lagged effect by 1 d. While the Singaporean residential electricity consumption is typically influenced predominantly by the weather ( 23 – 25 ), we find that in the early stages of the pandemic, progression of the disease has the most influence. This influence diminishes progressively as the country transitions into a strict lockdown—termed as the “Circuit Breaker”—in early April 2020, when people were only allowed to leave their homes for essential activities, such as grocery shopping and exercise.…”

mentioning

confidence: 47%

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

Raman

Peng

2021

Proc. Natl. Acad. Sci. U.S.A.

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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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mentioning

confidence: 47%

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

Raman

Peng

2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Our study shows that a strong correlation exists between the household peak consumption and new reported COVID-19 cases, and that there is a lagged effect by one day. While the Singaporean residential electricity consumption is typically influenced predominantly by the weather [19], we find that in the early stages of the pandemic, the disease progression has the most influence. This influence diminishes progressively as the country transitions into a strict lockdown-termed as the "Circuit Breaker"-in early April 2020, when people were only allowed to leave their homes for essential activities such as grocery shopping and exercise.…”

Section: Introductionmentioning

confidence: 68%

“…An important question now arises about the above observations: is it possible that the increase in the peak demand is not due to the response of Singaporeans towards COVID-19 progression, but only caused by changes in the weather? We ask this because studies in the past have shown that the Singaporean electricity consumption mainly depends on the weather, with the demand generally increasing with the temperature (e.g., see [19]). Therefore, do the correlations shown in Figs.…”

Section: Proactive Citizen Response Before the Circuit Breakermentioning

confidence: 99%

“…Weather data. Since the Singaporean electricity consumption is influenced by the weather [19], we included weather as an influencing factor while analyzing the change in the residential demand using our VECM. The weather data used in this study was obtained from Meteoblue [38].…”

Section: Data Collectionmentioning

confidence: 99%

“…While the original dataset did not specify the class of the households, i.e., the dwelling type, we used the average monthly electricity consumption statistics [37] Weather data. Since the Singaporean electricity consumption is influenced by the weather [19], we included weather as an influencing factor while analyzing the change in the residential demand using our VECM. The weather data used in this study was obtained from Meteoblue [38].…”

Section: Data Collectionmentioning

confidence: 99%

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

Raman

Peng

2020

Preprint

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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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Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting

Xiao

Zhang

et al. 2022

Energy

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Demand baseline estimation using similarity‐based technique for tropical and wet climates

Cited by 9 publications

References 18 publications

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

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

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

Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting

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