Pattern recognition algorithms for electricity load curve analysis of buildings

a b s t r a c tThis paper provides an analysis of the socio-economic and dwelling factors contributing to high electrical energy demand in UK domestic buildings. The socio-economic, dwelling and electricity consumption data were collected during a large-scale, city-wide survey, carried out in Leicester, UK, in 2009-2010. Annual electrical energy demand was estimated for 315 dwellings and an odds ratio analysis used to identify the socio-economic and dwelling factors that led to high electricity consumption. The effects of a number of socio-economic and dwelling factors which have not previously been studied for the UK domestic sector are included. Thus, for the first time, presence of teenagers, having electric space heating as the primary form of heating, portable electric heating and electric water heating were identified as significant drivers of high electricity demand in UK homes. The employment status and education level of the Household Representative Person, the number of floors in a dwelling, presence of fixed electric heating, and the proportion of low-energy lighting were shown to have no effect on high electricity consumption in UK homes. Given the impetus to reduce electricity consumption and CO 2 emissions from the domestic sector, these observations can help shape energy saving campaigns and future energy policy.

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“…[9,14,24,29,46]) using data mining (e.g. [48][49][50][51][52]), regression (e.g. [11,19,24,30,37,46]) and econometric methods (e.g.…”

Section: Socio-economic and Dwelling Factors Affecting Domestic Electmentioning

confidence: 99%

Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics

Jones

Lomas

2015

Energy and Buildings

101

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“…Other studies emphasized load scheduling from on peak to off peak times as an effective tool for consumption reduction with the additional economic and environmental gains [5,6] or to achieve financial savings for the customers [7,8]. Panapakidis et al indicates that the wider deployment of smart meters is an important tool that can enhance DSM implementation and reduce household consumption [9]. They emphasize the main advantage of the smart meters that allow for automatic collection of in depth information about the customer's behaviour thereby promoting new opportunities for energy saving and efficient management [9].…”

Section: Introductionmentioning

confidence: 97%

“…Panapakidis et al indicates that the wider deployment of smart meters is an important tool that can enhance DSM implementation and reduce household consumption [9]. They emphasize the main advantage of the smart meters that allow for automatic collection of in depth information about the customer's behaviour thereby promoting new opportunities for energy saving and efficient management [9].…”

Section: Introductionmentioning

confidence: 99%

Reducing Household Electricity Consumption through Demand Side Management: The Role of Home Appliance Scheduling and Peak Load Reduction

Laicāne

Blumberga

et al. 2015

Energy Procedia

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One of the key issues arising from extended smart metering roll-out across Europe is how the data obtained from the meters may be used to provide end users with important information on energy-saving advice, which is one of the main principles of demand side management (DSM). The investigation of the potential for demand side management to reduce peak load were evaluated in this study. One four-person household was surveyed with the aim to find out user activities within the house, in particular, the use of appliances. Two appliances -a washing machine and dishwasher -were selected for assessing the potential for load shifting. The derived results show that, as a result of washing machine and dishwasher load shifting, the peak load of a dwelling can be reduced on average by 24 % and 13.5 %, respectively.

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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 Methodology 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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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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Pattern recognition algorithms for electricity load curve analysis of buildings

Cited by 90 publications

References 14 publications

Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics

Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics

Reducing Household Electricity Consumption through Demand Side Management: The Role of Home Appliance Scheduling and Peak Load Reduction

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

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