Datum
2021-05-20Schlagwort
330 Wirtschaft 333 Boden- und Energiewirtschaft WirtschaftIndustrieKorrelationWärmebedarfProfilMetadata
Zur Langanzeige
Aufsatz
Annual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysis
Zusammenfassung
An accurate method to predict annual heat load profiles is fundamental to many studies, e.g., preliminary design or potential studies on renewable heating systems. This study presents a method to predict annual heat load profiles with a daily resolution for industry and commerce, based on an analysis of 797 natural gas load profiles (≥1.5 GWh/a). To derive heat load profiles, these natural gas load profiles are normalized and those with a potentially non-linear relationship between heat demand and natural gas consumption are excluded. The heat
load profiles are clustered using the k-means algorithm according to their respective dependency on mean daily ambient temperature. The results reveal that the heat demand of most consumers is characterized by a clear dependency on mean daily ambient temperature, even in industry. The assignment of the load profiles to the clusters can be explained by the respective composition of each consumers’ heat sinks. In a regression analysis, individual regressions for each load profile are only slightly more accurate than the regressions for all load profiles assigned to one of the respective clusters. In terms of accuracy and user-friendliness, the developed cluster regression-based correlations for load profile prediction offer a significant improvement on previous methods.
load profiles are clustered using the k-means algorithm according to their respective dependency on mean daily ambient temperature. The results reveal that the heat demand of most consumers is characterized by a clear dependency on mean daily ambient temperature, even in industry. The assignment of the load profiles to the clusters can be explained by the respective composition of each consumers’ heat sinks. In a regression analysis, individual regressions for each load profile are only slightly more accurate than the regressions for all load profiles assigned to one of the respective clusters. In terms of accuracy and user-friendliness, the developed cluster regression-based correlations for load profile prediction offer a significant improvement on previous methods.
Zitierform
In: Energy conversion and management: X Volume 10 (2021-05-20) eissn:2590-1745Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202106304177,
author={Jesper, Mateo and Pag, Felix and Vajen, Klaus and Jordan, Ulrike},
title={Annual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysis},
journal={Energy conversion and management: X},
year={2021}
}
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2021-06-30T08:44:42Z 2021-06-30T08:44:42Z 2021-05-20 doi:10.17170/kobra-202106304177 http://hdl.handle.net/123456789/12949 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ Annual heat load profiles Correlations Industry Commerce k-means clustering Standard loaf profiles 330 333 Annual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysis Aufsatz An accurate method to predict annual heat load profiles is fundamental to many studies, e.g., preliminary design or potential studies on renewable heating systems. This study presents a method to predict annual heat load profiles with a daily resolution for industry and commerce, based on an analysis of 797 natural gas load profiles (≥1.5 GWh/a). To derive heat load profiles, these natural gas load profiles are normalized and those with a potentially non-linear relationship between heat demand and natural gas consumption are excluded. The heat load profiles are clustered using the k-means algorithm according to their respective dependency on mean daily ambient temperature. The results reveal that the heat demand of most consumers is characterized by a clear dependency on mean daily ambient temperature, even in industry. The assignment of the load profiles to the clusters can be explained by the respective composition of each consumers’ heat sinks. In a regression analysis, individual regressions for each load profile are only slightly more accurate than the regressions for all load profiles assigned to one of the respective clusters. In terms of accuracy and user-friendliness, the developed cluster regression-based correlations for load profile prediction offer a significant improvement on previous methods. open access Jesper, Mateo Pag, Felix Vajen, Klaus Jordan, Ulrike doi:10.1016/jecmx.2021.100085 Wirtschaft Industrie Korrelation Wärmebedarf Profil publishedVersion eissn:2590-1745 Energy conversion and management: X Volume 10 false 100085
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