Short-Term Load Forecasting, Profile Identification, and Customer Segmentation: A Methodology Based on Periodic Time Series

Espinoza, Marcelo; Joye, C; Belmans, Ronnie; Moor, Bart De

doi:10.1109/tpwrs.2005.852123

Cited by 227 publications

(125 citation statements)

References 24 publications

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“…Various analysis methodologies have been developed: they have used the regression model [37][38][39][40]; time-series analysis [41][42][43][44][45][46][47][48][49]; and clustering techniques [46][47][48][49][50][51][52][53][54]. However, most analyses have been aimed at short-and medium-term demand forecasting; relatively few analyses have been directed at tailor-made feedback.…”

Section: Analysis Methodsology Of Residential Electricity-use Datamentioning

confidence: 99%

“…Their analysis identified persistent daily routines and patterns of consumption or baselines typical for specific weather or daily conditions, e.g., hot working days and cold weekend days. From a power network manager point of view, Espinoza et al [49] developed a methodology aiming for both short-term forecasting and customer profile identification. They identified eight clusters from 245 pieces of hourly load data from a HV-LV substation within the Belgian grid, using a periodic autoregression model and a clustering technique.…”

Section: Analysis Methodsology Of Residential Electricity-use Datamentioning

confidence: 99%

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Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

2017

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Abstract:Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption-as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

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Section: Analysis Methodsology Of Residential Electricity-use Datamentioning

confidence: 99%

Section: Analysis Methodsology Of Residential Electricity-use Datamentioning

confidence: 99%

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

2017

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“…The number of clusters and the time series used for each aggregator are determined via KSC (Alzate & Sinn 2013). The forecasting model used is a periodic autoregresive model with exogenous variables (PARX) (Espinoza et al 2005). The results show an improvement of 20.55% with the similarity based on Spearman's corrleation in the forecasting accuracy compared to not using clustering at all (i.e., aggregating all smart meters).…”

Section: Power Load Clusteringmentioning

confidence: 99%

Kernel Spectral Clustering and Applications

Langone

Mall

Alzate

et al. 2016

Unsupervised Learning Algorithms

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“…Kmeans and self-organizing maps (SOMs) were applied in [14] to analyze load profiling in order to provide more customized tariff offers for customers pricing and supply availability. A profile identification, short-term load forecasting, and customer segmentation methodology was proposed in [15] to compute the daily profiles from estimated models. Another cloud-based Dynamic Demand Response (D 2 R) platform was introduced in [3] to perform curtailment strategy selection, intelligent demand-side management and forecasting, visualize LP patterns, and to relieve peak load.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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Short-Term Load Forecasting, Profile Identification, and Customer Segmentation: A Methodology Based on Periodic Time Series

Cited by 227 publications

References 24 publications

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

Kernel Spectral Clustering and Applications

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

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