Characterizing patterns and variability of building electric load profiles in time and frequency domains

Li, Han; Wang, Zhe; Hong, Tianzhen; Parker, Andrew; Neukomm, Monica

doi:10.1016/j.apenergy.2021.116721

Cited by 36 publications

(8 citation statements)

References 25 publications

(27 reference statements)

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“…14 . The definitions of those two parameters can be found in this paper 39 . The high-load durations are the number of hours in a day when the building’s electrical load is at high level, which usually overlaps with the operation hours.…”

Section: Technical Validationmentioning

confidence: 99%

A synthetic building operation dataset

Wang

Hong

2021

Sci Data

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This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

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Section: Technical Validationmentioning

confidence: 99%

A synthetic building operation dataset

Wang

Hong

2021

Sci Data

Self Cite

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“…This paper aims to contribute to this field of research by focusing on demand profiles in more detail, to investigate common demand profiles occurring behind such group averages, and the changes in their prevalence over time, for a broadly representative sample of British households, through the application of cluster analysis to their smart meter data. A substantial body of research has arisen that applies cluster analysis to granular smart meter data to identify distinct but commonly occurring demand profiles in the domestic, as well as industrial and commercial, sectors [7][8][9][10]. The aim of cluster analysis is to take a set of cases and segment them into a number of groups (clusters), such that cases within a group are more similar to each other than they are to those in other groups.…”

Section: Introductionmentioning

confidence: 99%

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

Pullinger,

Zapata-Webborn,

Kilgour

et al. 2023

Preprint

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This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0°C, and few fitting it above 14°C. COVID-19 pandemic restrictions on work and schooling are associated with households’ patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking.

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“…In addition to this study, methods of clustering and grading mechanisms were also applied in other studies. 23,24 Moreover, clustering and other similar classification methods were used in various types of research, such as abnormal energy data recognition, 25 equipment operating status recognition, 26 and building energy demand or consumption prediction. 27 Although relevant research made some progress, there were still some challenges regarding clustering algorithms that needed to be solved.…”

Section: Introductionmentioning

confidence: 99%

Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings

Zhao

Zhang

Ujeed

et al. 2022

Building Services Engineering Research and Technology

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The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction. Practical application This paper proposed a matrix model method based on the conventional K-nearest-neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method was applied to various buildings online, which coupled the process of filtering historical data and flexible selectivity of models when used on different buildings. This method was used for assessing energy-saving potential, energy-saving retrofit priorities, and power consumption forecasting, which will benefit researchers and engineers.

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Characterizing patterns and variability of building electric load profiles in time and frequency domains

Cited by 36 publications

References 25 publications

A synthetic building operation dataset

A synthetic building operation dataset

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings

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