2021
DOI: 10.1016/j.energy.2020.118962
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Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

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Cited by 18 publications
(7 citation statements)
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“…Regarding the geographical dimension, because of the lack of granular data on the geography, location, and demand loads, researchers and institutions have filled this gap by developing databases that offer an aggregated or typical view of the daily behavior of a consumer (Standard Load Profiles or SLP), the typical annual consumption of different consumers, and the consumption in a region based on the top-down calculations based on economic activities [22]. These databases are usually developed from disaggregation of data by class, and they do not always coincide with reality [23].…”
Section: Research Problemmentioning
confidence: 99%
“…Regarding the geographical dimension, because of the lack of granular data on the geography, location, and demand loads, researchers and institutions have filled this gap by developing databases that offer an aggregated or typical view of the daily behavior of a consumer (Standard Load Profiles or SLP), the typical annual consumption of different consumers, and the consumption in a region based on the top-down calculations based on economic activities [22]. These databases are usually developed from disaggregation of data by class, and they do not always coincide with reality [23].…”
Section: Research Problemmentioning
confidence: 99%
“…Energy storages with high energy density, such as electrochemical batteries and LH2 tanks, can address the energy mismatch issue and provide power supply reliability. Furthermore, demand-side management strategies in accordance with renewable power characteristics and dynamic grid information can provide energy flexibility from the multi-energy systems [14]. The aggregated electric vehicles can become flexible sources and enhance system resilience, with the deferrable load and large storage capacity [15] [16].…”
Section: Introductionmentioning
confidence: 99%
“…Using smart meter data, a classification method based on an agglomerative clustering algorithm aiming at detecting customers with photovoltaic units has been presented in [19]. In [20], a combination of partitioning and hierarchical clustering algorithms aiming at calculating demand-side management flexibility potential of industries using hourly electricity consumption data of smart meters has been presented.…”
Section: Introductionmentioning
confidence: 99%