A Comprehensive Review of Residential Electricity Load Profile Models

Proedrou, Elisavet

doi:10.1109/access.2021.3050074

Cited by 87 publications

(60 citation statements)

References 57 publications

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“…We have to consider the possibility that both comparison profiles are currently not entirely representative for Germany either, because of the age of the SLP and the limited scope of the HTW measurements (only single-family houses that are all in spatial proximity). The SLP is estimated to show a deviation to the actual consumption of more than 20% [3], which is similar to our results. Analyzing the simulation results of Germany and the United Kingdom already shows a strength of our approach, because we are able to represent very different shares of enduses in the overall load profile.…”

Section: Transformation To German Working Group On Energysupporting

confidence: 90%

“…We see a trend of using weather data as the central input to predict regional or national load profiles [44,45]. These models reduce the amount of input data required with an improved methodology, which is motivated by the lack of detailed, cross-country measurement datasets [3]. Although some load profiles developed specifically for one single country might perform better, we offer an approach that can be adopted to all regions where TUS data are available.…”

Section: Discussionmentioning

confidence: 99%

“…Generating profiles on a regional or national level with bottomup models then requires an extrapolation of the results for single buildings using socio-economic or building stock data. In contrast, top-down models use aggregated data and correlate them to macro-variables such as weather conditions or consumer behavior [2,3].…”

Section: Introductionmentioning

confidence: 99%

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A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

2021

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End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

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Section: Transformation To German Working Group On Energysupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

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A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

2021

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“…The aggregated value of the 1-second time- HE power distribution grids are facing major structural and operational transformations due to increasing deployment of distributed energy resources (DER), especially renewable energy resources (RES) at low voltage (LV) feeders on top of changes in the typical LV load profiles. These are associated with higher dynamical change in the operation of the network [1], [2]. The tool at hand for the Distribution System Operators (DSOs) for monitoring and operation of this part of the power gird is the Distribution Management Systems (DMS), which has as core sub-system a Meter Data Management System (MDMS) collecting and analyzing data from advanced metering infrastructures (AMI) and from Smart Meters (SMs) [3].…”

mentioning

confidence: 99%

“…In the following, we denote as "instantaneous" active power at a given moment t the active power evaluated over several periods (T=20 ms in Europe) of the voltage and current signals and reported at the end of the time interval (e.g., every 1 second). Despite the acknowledged need of the DSO for closer observability of the dynamics in each node of the LV network [1], [7] the challenge arise when mitigating: (a) costs (e.g. expensive solution to deploy AMI at each MV/LV node to ensure system observability); (b) low quality information from already available infrastructure (e.g data from SM, if available, comes asynchronously in aggregated form every 60-or 30minutes, thus limiting the observability of quantities such the rms values of voltage and current, "instantaneous" active and reactive power [7], [8] reported every 1s; and (c) privacy and cyber security concerns of grid users in providing access to high resolution data collected from SM [9].…”

mentioning

confidence: 99%

High Reporting Rate Smart Metering Data for Enhanced Grid Monitoring and Services for Energy Communities

Sănduleac

Ciornei

Toma

et al. 2022

IEEE Trans. Ind. Inf.

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Capturing the dynamic behavior of the power distribution grids, especially under high penetration of renewables, is of high interest for grid operators. The distribution power grids are not fully observable due to lack of sufficient metering infrastructure, especially downstream of medium voltage substations. Therefore, fusion of data recorded at significantly different reporting rates was proposed to increase the situational awareness of the system with non-negligible effect on the accuracy of the monitoring tool. Higher reporting rates are possible for next generation smart meters, but they raise higher concerns about data privacy, already an issue for smart meters rollout. This work proposes a framework for knowledge extraction from high reporting-rate smart metering data. The process takes place at smart meter level and with low computation and communication costs and preserving user privacy, with the scope to increase the accuracy of the monitoring tools for distribution power grids. The methodology makes use of statistical metrics able to capture system dynamics relevant for network diagnosis. The proposed approach is validated on a three-phase low voltage power flow model applied to a realistic testbed microgrid and real field measurements synchronized at one second.Index Terms-data privacy, dynamic behavior of power grids, high reporting rate smart meters, quality of supply, technological knowledge extraction. NOMENCLATUREVariables: p Net active power (kW) defined as difference between self generation and consumption at prosumers' nodes; defined over a time interval, usually reported at 1 s. qReactive power (kvar); u Voltage amplitude (line to neutral) (V); i Line current, rms value (A); Active power absorbed from the grid reported at time t and measured by the k-th meter; Active power injected into the grid reported at time t by the k-th meter; Net-active power exchanged with the grid reported at time t by the k-th meter;

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Energy‐saving technologies and energy efficiency in the post‐COVID era

Gorina,

Korneeva,

Kovaleva

et al. 2024

Sustainable Development

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The COVID‐19 pandemic made people reevaluate their energy consumption and energy efficiency. It held up a mirror to humanity's opportunistic and ruthless deployment of energy sources. As the city streets went empty and the international air traffic stopped altogether, people witnessed the significant drop in CO2 emissions, realized their dependence upon energy sources, and became aware of global climate change. In addition, with lockdowns and home offices, the demand for energy and the usual peak hours shifted offering a new perspective in energy consumption. The pandemic delivered a devastating blow to the human belief in having the constant and everlasting supply of energy that can be relied upon in everything. All these attracted the attention of researchers focusing on energy consumption and efficiency during the COVID‐19 pandemic. As a result, the vast body of academic literature on the topic burgeoned in the past four years yielding many exciting perspectives and viewpoints. Our paper conducts a comprehensive review of this body of literature employing the bibliometric network analysis of 12960 publications indexed in Web of Science database. It demonstrates the potential benefits and challenges associated with implementing energy‐saving technologies, altering energy consumption behavior, and implementing novel ICT solutions in a post‐COVID world. In addition, it highlights the importance of energy efficiency measures and examines novel technologies that can contribute to a sustainable and resilient energy future. Our findings emphasize the need for robust policies, technological advancements, and public engagement for fostering energy efficiency and mitigating environmental impacts of post‐pandemic energy consumption.

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A Comprehensive Review of Residential Electricity Load Profile Models

Abstract: Date of publication xxxx xx, xxxx, date of current version xxxx xx, xxxx.

Cited by 87 publications

References 57 publications

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

High Reporting Rate Smart Metering Data for Enhanced Grid Monitoring and Services for Energy Communities

Energy‐saving technologies and energy efficiency in the post‐COVID era

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