Consumer Segmentation and Knowledge Extraction from Smart Meter and Survey Data

Wijaya, Tri Kurniawan; Ganu, Tanuja; Chakraborty, Dipanjan; Aberer, Karl; Seetharam, Deva P.

doi:10.1137/1.9781611973440.26

Cited by 41 publications

(18 citation statements)

References 16 publications

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“…See, e.g., [21], [24], [26], [39]. Percentage improvement in the NRMSE of the CBAF strategy (compared to the traditional aggregate forecast, k = 1) of 500, 1,000, 2,000, and 3,639 customers over a different number of clusters and clustering methods (the higher the better).…”

Section: Related Workmentioning

confidence: 99%

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Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Abstract-While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

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Section: Related Workmentioning

confidence: 99%

“…More specifically, it contains the information of how end users consume electricity in near real time. However, this also means that utility companies worldwide face challenges on managing big (smart meter) data on their hands of at least big volume, big velocity, and big value, whose benefits are waiting to be discovered [39].…”

Section: Introductionmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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“…[20,21] presents studies on the prediction of household information based on smart meter data. In [22,23] consumptions profiles obtained via clustering are correlated to household characteristics in a similar fashion to what is done in this work.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy clustering and prediction of electricity demand based on household characteristics

Viegas¹,

Vieira²,

Sousa³

2015

Advances in Intelligent Systems Research

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The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into valuable knowledge on the behaviour of consumers. This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data using fuzzy clustering and modelling. The methodology intends to: (1) determine consumer segments based on the metering data using the fuzzy c-means clustering algorithm, and (2) develop Takagi-Sugeno fuzzy models in order to predict the demand profile of the consumers.

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“…For example, smart meter data has been used for consumer segmentation in [15] and [16]. A body of literature also exists on baseline consumption estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Hence it represents a chance-constrained mixed integer program. The expected values appearing in equations (14) and (15) can be evaluated using (13), resulting in deterministic versions of these equations given by (16) and (17), respectively. It should be noted that by setting p i,t,d to 1, we recover the deterministic framework presented in Section 4.1.…”

Section: Stochastic Frameworkmentioning

confidence: 99%

iDR

Chandan

Ganu

Wijaya

et al. 2014

Proceedings of the 5th International Conference on Future Energy Systems

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Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data.In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers' houselevel consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider's DR objectives -such as reduction in peak load -while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR's capabilities are demonstrated using real-world house-level as well as appliance-level data.

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Consumer Segmentation and Knowledge Extraction from Smart Meter and Survey Data

Cited by 41 publications

References 16 publications

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Fuzzy clustering and prediction of electricity demand based on household characteristics

iDR

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