Lifestyle Segmentation Based on Energy Consumption Data

Kwac, Jungsuk; Flora, June A.; Rajagopal, Ram

doi:10.1109/tsg.2016.2611600

Cited by 56 publications

(34 citation statements)

References 22 publications

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“…Compared to euclidean distance, the EMD is more computationally expensive to calculate. Therefore, in [20], k-means and euclidean distance was first used to create a manageable initial sized dictionary of loads shapes, and then the dictionary entries were clustered with EMD, which allows for a ground-distance to be specified between the different hours of the day.…”

Section: Clustering Methodologymentioning

confidence: 99%

“…Additionally, different distance metrics have been used to specify the similarity between load profiles. While the euclidean distance between load profiles has been most commonly used, more recent work has employed shape based distance metrics for clustering load profiles, including Earth Mover's Distance [20], Dynamic Time Warping (DTW) distance [28,35] and k-shape distance [35]. The advantage of these shape-based metrics is that the different hours of the day are not treated as orthogonal by the similarity measure.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing household-level load forecasts using daily load profile clustering

Barbour¹,

González

2018

Proceedings of the 5th Conference on Systems for Built Environments

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Forecasting the electricity demand for individual households is important for both consumers and utilities due to the increasing decentralized nature of the electricity system. Particularly, utilities often have very little information about their consumers except for aggregate building level loads, without knowledge of interior details about the household appliance sets or occupants. In this paper, we explore the possibility of enhancing the day-ahead load forecasts for hundreds of individual households by clustering their daily load profile history to obtain each consumer's specific typical consumption patterns. The clustering method is based on load profile shape using the Earth Mover's Distance metric to calculate similarity between load profiles. The forecasting methods then predict the next day shape from the empirical probability of previous cluster transitions in the consumer's load history and estimate the magnitude either by using historical load relationships with temperature and forecast temperatures or previous day consumption levels. The generated forecasts are compared to a benchmark Multiple Linear Regression (MLR) day-ahead forecast and persistence forecasts for all individuals. While at the aggregate level the MLR method represents a significant improvement over persistence forecasts, on an individual level we find that the best forecasting model is specific to the individual. In particular, we find that the MLR model produces lower errors when consumers have a consistent daily temperature response and the cluster model with previous day magnitude produces lower errors for consumers whose consumption changes abruptly in magnitude for several days at a time. Our work adds to the state of knowledge surrounding individual household load forecasting and demonstrates the potential for cluster-based methodologies to enhance short term load forecasts.

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Section: Clustering Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing household-level load forecasts using daily load profile clustering

Barbour¹,

González

2018

Proceedings of the 5th Conference on Systems for Built Environments

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“…6 Then, the aggregator operates the physical storage by aggregating all the users' charge and discharge decisions. Note that we consider a daily operation of virtual storage sharing as well as the daily demand charge tariff for users' electricity bills, because users' electricity loads reflect their activities which are often periodic on a daily basis (see, e.g., extensive studies in [33] and [34]). Furthermore, users' load profiles can differ from one day to another (e.g., the differences between weekdays and weekends).…”

Section: B Storage Aggregatormentioning

confidence: 99%

Virtual Energy Storage Sharing and Capacity Allocation

Zhao

Wang

Huang

et al. 2020

IEEE Trans. Smart Grid

119

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Energy storage can play an important role in energy management of end users. To promote an efficient utilization of energy storage, we develop a novel business model to enable virtual storage sharing among a group of users. Specifically, a storage aggregator invests and operates the central physical storage unit, by virtualizing it into separable virtual capacities and selling to users. Each user purchases the virtual capacity, and utilize it to reduce the energy cost. We formulate the interaction between the aggregator and users as a two-stage optimization problem. In Stage 1, over the investment horizon, the aggregator determines the investment and pricing decisions. In Stage 2, in each operational horizon, each user decides the virtual capacity to purchase together with the operation of the virtual storage. We characterize a stepwise form of the optimal solution of Stage-2 Problem and a piecewise linear structure of the optimal profit of Stage-1 Problem, both with respect to the virtual capacity price. Based on the solution structure, we design an algorithm to attain the optimal solution of the two-stage problem. In our simulation results, the proposed storage virtualization model can reduce the physical energy storage investment of the aggregator by 54.3% and reduce the users' total costs by 34.7%, compared to the case where users acquire their own physical storage.

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“…Interestingly, beyond studies of income and other forms of resource allocation, the societal distribution of energy-relevant practices and related climate effects remain relatively underexplored. Recent approaches to segmenting groups on the basis of their smart-meter-generated energy consumption profiles in the household promise better insight into group-specific differences in that part of the lifestyle that occurs at home [31]. In order to shed some light on methods of lifestyle segmentation and to go beyond the portion that takes place at home, a number of aspects that are considered essential for energy-specific lifestyle research are discussed.…”

Section: Beyond Existing Research On Energy Lifestylesmentioning

confidence: 99%

Identifying Consumer Lifestyles through Their Energy Impacts: Transforming Social Science Data into Policy-Relevant Group-Level Knowledge

2019

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The analytical framework presented herein is based on the identification of social groups with distinct patterns in their energy-relevant behaviour. This was achieved by clustering individuals according to their primary energy demands in six main areas of life. Due to the close relationship between energy-relevant behaviour and associated impacts, the suggested approach is considered better suited for the identification of groups with actual differences in their climate and energy-related behaviour than conventional approaches that cluster individuals based on their psychological or sociodemographic characteristics. Moreover, it is assumed that this focus on energy-relevant behaviour leads to a higher measuring equivalence in a country comparison or in a longitudinal setting. From an analytical point of view, the most significant benefit of the presented method over conventional lifestyle typologies is that all psychological, cultural and sociodemographic factors can be used as explanatory variables without resulting in circular reasoning. In terms of required data, the approach was designed around what could be collected by conventional survey methods. Variables such as energy use and emissions were calculated by the means of life cycle assessment (LCA) based on self-reported behaviour and equipment use.

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Lifestyle Segmentation Based on Energy Consumption Data

Cited by 56 publications

References 22 publications

Enhancing household-level load forecasts using daily load profile clustering

Enhancing household-level load forecasts using daily load profile clustering

Virtual Energy Storage Sharing and Capacity Allocation

Identifying Consumer Lifestyles through Their Energy Impacts: Transforming Social Science Data into Policy-Relevant Group-Level Knowledge

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