Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

Debnath, Ramit; Bardhan, Ronita; Misra, Ashwin; Hong, Tianzhen; Rozite, Vida; Ramage, Michael

doi:10.1016/j.enpol.2022.112886

Cited by 11 publications

(8 citation statements)

References 49 publications

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“…China's power consumption has been on the rise, which is approximately a smooth exponential curve. It can be seen that the annual net growth of China's power consumption shows an upward trend on the whole [17]. However, the net growth of China's power consumption showed a downward trend, which was closely related to the macroeconomic form of our country at that time.…”

Section: Introductionmentioning

confidence: 87%

Evaluation and Analysis of Electric Power in China Based on the ARMA Model

2022

Mathematical Problems in Engineering

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With the rapid development of China’s economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China’s electricity consumption, this paper analyzes the data characteristics of China’s electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China’s power demand from 2004 to 2019, the ARMA time series model is used to analyze China’s future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China’s electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China’s power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China’s power.

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Section: Introductionmentioning

confidence: 87%

Evaluation and Analysis of Electric Power in China Based on the ARMA Model

2022

Mathematical Problems in Engineering

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“…This paper proposes Gaussian Mixture Model (GMM) as a novel method to identify energy consumption patterns of public buildings throughout the pandemic. The reliability of this method has been proved in other studies [ [23] , [24] , [25] , [26] , [27] ], and compared with traditional clustering analysis method, the model has a wider range of application. For example, GMM can cluster two classes with the same mean (same clustering center point), but the k-means clustering algorithm has limitations on this.…”

Section: Introductionmentioning

confidence: 90%

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Huang¹,

Gou²

2023

Journal of Building Engineering

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“…Still, developing country contexts require reasonable interpretation due to rapid growth, and structural changes brought about by development and government interventions, which can generate volatile income and price elasticity (Holtedahl and Joutz, 2004; Yépez-García et al , 2011). Additionally, recent studies observe changes in demand caused by measures to restrict mobility such as lockdowns and remote work due to the coronavirus (SARS-CoV-2) pandemic (Debnath et al , 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Still, developing country contexts require (Holtedahl and Joutz, 2004;Y epez-García et al, 2011). Additionally, recent studies observe changes in demand caused by measures to restrict mobility such as lockdowns and remote work due to the coronavirus (SARS-CoV-2) pandemic (Debnath et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Income and price elasticity of residential electricity demand in Latin America and the Caribbean: a meta-analysis and meta-regression analysis

Marques

Uhr

2023

IJESM

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Purpose This paper aims to identify the income and price elasticities of demand for residential electricity in Latin America and the Caribbean (LAC) and to verify their main determinants. Design/methodology/approach Meta-analysis and meta-regression methods were applied. After collecting and filtering journal articles, the authors obtained a sample composed of 76 studies covering 1979–2020. Findings The results show that the LAC's income elasticity is approximately 0.20 and 0.92 for the short and long term, respectively. The LAC's price elasticity is approximately −0.37 and −0.46 for the short and long term, respectively. Furthermore, the estimates are affected by the data structure, the estimation method used and the sampling period. Originality/value The authors close a gap in the literature by analyzing the price and income elasticities of demand through meta-analysis and meta-regression.

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Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

Cited by 11 publications

References 49 publications

Evaluation and Analysis of Electric Power in China Based on the ARMA Model

Evaluation and Analysis of Electric Power in China Based on the ARMA Model

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Income and price elasticity of residential electricity demand in Latin America and the Caribbean: a meta-analysis and meta-regression analysis

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