Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

Kavousian, Amir; Rajagopal, Ram; Fischer, Martin

doi:10.1016/j.energy.2013.03.086

Cited by 465 publications

(250 citation statements)

References 44 publications

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“…For example, how far out does the impact on energy consumption of changes in human mobility in a typical region i extend? Although research has examined various effects on energy consumption across building types [7,8,13,18,21,30], our study confirmed that there are spatial effects due to human activities as indicated by human mobility [1,15,32] across building types, which have been overlooked by the literature. In order to cope with the continuing growth in population [33] and the corresponding increase in urban activity levels, we need to develop a better understanding of the root causes of energy consumption.…”

Section: Discussionsupporting

confidence: 69%

“…Energy consumption rates cannot be regarded as being independently generated at a building level and arising solely as a result of building characteristics [4,6,7,8,9,13,18,21,30]. Possible spillover effects have to be taken into account across neighboring buildings.…”

Section: Discussionmentioning

confidence: 99%

“…In another effort to explain variations of energy consumption in residential buildings, Kavousian et al [21] considered external conditions such as weather and building location in their statistical model measuring the underlying determinants of daily energy use. These efforts reaffirm the need to assess the energy consumption of buildings in their urban context, taking into account their existing surroundings and any urban dynamics they are likely to encounter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Smarter Cities: Linking Human Mobility and Energy Use Fluctuations across Building Types

Taylor¹,

Wei²

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Smarter Cities: Linking Human Mobility and Energy Use Fluctuations across Building Types

Taylor¹,

Wei²

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Such kinds of buildings emphasize the importance of privacy, with high, sound-proof windows, which therefore requires a stronger cooling system. Some previous studies have found that the dwelling type [17,[35][36][37] and the number of bedrooms [17,35] have a significant influence over residential electricity consumption.…”

Section: The Factors Affecting Residential Electricity Consumption Bamentioning

confidence: 99%

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

Chen

2017

Sustainability

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This article attempts to examine the factors affecting residential electricity consumption through two approaches, a socioeconomic perspective and a direct use perspective. The results of this study find that gross domestic product (GDP), employment rates, residential space, and the implementation of energy labeling schemes provide significant impacts on residential electricity consumption. However, the impacts of electricity price and the energy efficiency standards do not receive significant support. The analysis of the direct use approach finds that air conditioners consume the largest portion of electricity, amounting to 1470 kWh for each household and accounting for 26.81% except for lighting. Refrigerators and rice cookers follow, consuming 815.83 kWh (14.88%) and 343.85 kWh (6.27%) of electricity. The correlation analysis reveals that GDP keeps a high relationship with the installation of electrical appliances and eventually leads to an increase in residential electricity consumption. By integrating the results obtained from these two approaches, this paper also discusses some improvement strategies. The results, derived from the case example of Taiwan's residential electricity consumption, may provide valuable information for policy-making involving green labeling, energy standards, and electricity prices.

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“…Many of the papers apply K-Means for baseline clustering and compare more advanced methods to this baseline [14][15][16], with inconclusive outcomes regarding the best method for clustering. Some papers make an effort to preprocess the smart meter data; popular preprocessing methods are principal component analysis and factor analysis for dimensionality reduction [17,18] and self-organizing maps for 2 Dimensional representation of the data [3,10]. All identified methods are not particularly well-suited to time series data, such as smart meter data.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Clustering Using Smart Meter Data

2018

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Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method.

show abstract

Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

Cited by 465 publications

References 44 publications

Towards Smarter Cities: Linking Human Mobility and Energy Use Fluctuations across Building Types

Towards Smarter Cities: Linking Human Mobility and Energy Use Fluctuations across Building Types

The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan

Electricity Consumption Clustering Using Smart Meter Data

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