Modeling and analysis of the electricity consumption profile of the residential sector in Spain

Escobar, Patricio Carrillo; Martínez, E.; Sáenz-Díez, J.C.; Jiménez, E.; Blanco‐Fernández, Julio

doi:10.1016/j.enbuild.2019.109629

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…Nonetheless, one of the most important factors in the household energy consumption still is the patterns of use and occupancy, attributed to occupant's behaviour [3][4][5]. The energy consumption due to occupancy is determined by a series of variables such as the number and age of occupiers, the proportion of time residents spend in their homes, the way that they inhabit the indoor spaces (e.g., lights on, multiple use of electrical appliances and equipment), activities conducted (e.g., cooking, sleeping) and the amount of electrical equipment and its usage.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Lockdown Due to COVID-19 on the Electricity Consumption of a Housing Development in the UK

Tubelo

Naghiyev

Gillott

et al. 2021

Sustainability in Energy and Buildings 2021

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In March 2020, the United Kingdom (UK) government ruled that householders must stay home as a response to the COVID-19 outbreak to help flatten the curve of the epidemic and reduce the exponential growth of the virus. Commercial activities, workplaces and schools were obliged to temporarily close in compliance with the government rules. This first and most restrictive lockdown took place from late March to early May 2020 when occupants had to stay in their homes except for very restricted essential activities. Two other lockdowns were introduced in November 2020 and January 2021, alongside with a range of restrictive measures during 2020. This offered an unprecedented opportunity to investigate the impact of a prolonged period of occupancy on household electricity consumption. In this work, the authors compared electricity consumption data collected from 21 energy-efficient houses in Nottingham, UK, during these lockdown periods to the same period in previous year. The findings indicated that the monthly electricity consumption in April 2020, during the strictest lockdown, increased approximately 5% in comparison to the same period in 2019. The daily electricity consumption profile during this lockdown showed earlier and longer peaks in the evenings with the emergence of a new midday peak in comparison to typical daily peaks prior to lockdown. The findings supported the idea that electricity consumption is increased as a result of the proportion of time residents spend in their homes and this corresponded to 17% more in 2020-2021, when restrictive measures were in place.

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Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Lockdown Due to COVID-19 on the Electricity Consumption of a Housing Development in the UK

Tubelo

Naghiyev

Gillott

et al. 2021

Sustainability in Energy and Buildings 2021

View full text Add to dashboard Cite

show abstract

“…The household sector is a key sector in the current energy grid in the European Union (EU). Electricity consumption in this sector amounts to 29 % of total consumption [17]. In Latvia, this percentage is slightly lower -it is about 25-26% of the total electricity consumption.…”

Section: Analysis Of the Latvian Household Electricity Consumptionmentioning

confidence: 92%

Potential of End-User Electricity Peak Load Shift in Latvia

Ļebedeva

Borodiņecs

Krumins³

et al. 2021

Latvian Journal of Physics and Technical Sciences

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One of the main challenges in the world as well as in Latvia is the reduction of the fossil fuel consumption. Electricity generation, which is still mainly produced by fossil fuel or nuclear power, is one of the largest shares. Nowadays end-users can efficiently control their electricity consumption by shifting electrical loads and taking into account fluctuating electricity price at Nord Pool. The smart metering systems have been expanding, but for the time being, end-users do not use this possibility efficiently. This study provides an analysis of Latvian electricity consumption specifics and evaluates a potential to shift electricity peak loads. The study includes households, industry and commercial/public sector. Based on the questionnaire distributed among end-users, the initial data on equipment and usage profile were collected. The most essential equipment was reviewed to estimate the potential for load shift. The obtained results show that that there is a positive effect of the load shift. In the household sector, the average electrical load shift can be 6 % (95 GWh/ year) during a period at a cheaper price; in the industry sector on average 37 % (36.58 GWh/ year) and in the commercial/public sector there is no potential due to its specifics (working hours, constant use of refrigerators, etc.).

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“…Pattern identification is a major approach to understanding the occupant-behavior-related energy profiles in buildings (Yang et al 2018a;Wen et al 2019;Quintana et al 2020;Popoola and Chipango 2020), and is widely applied in both residential and non-residential buildings. This provides insight for distinguishing different types of users or (Sala et al 2019;Escobar et al 2020). The promotion of smart meters in households has made it possible to acquire hourly or sub-hourly data from massive number of households, which are used to support pattern analysis (Zhou et al 2017a).…”

Section: Parametric Analysis At the Design Phasementioning

confidence: 99%

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

et al. 2020

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Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

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Modeling and analysis of the electricity consumption profile of the residential sector in Spain

Cited by 25 publications

References 30 publications

Assessing the Impact of Lockdown Due to COVID-19 on the Electricity Consumption of a Housing Development in the UK

Assessing the Impact of Lockdown Due to COVID-19 on the Electricity Consumption of a Housing Development in the UK

Potential of End-User Electricity Peak Load Shift in Latvia

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

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