Data repository: Annual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysis

Published: 18 March 2021| Version 1 | DOI: 10.17632/nwxv38dxsr.1
Contributors:
Mateo Jesper,
Felix Pag,
Klaus Vajen,
Ulrike Jordan

Description

This data repository includes results of the original research article “Annual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysis”. In this article, a method to predict annual heat load profiles with a daily resolution for large consumers from industry and commerce is developed. This method is based on a cluster and regression analysis of natural gas load profiles from 797 German consumers, most with a consumption of more than 1.5 GWh/a. The data repository contains plots of all 797 original normalized load profiles and predicted normalized load profiles in the form of time series. The correlation between daily mean ambient temperature and daily normalized natural gas consumption is visualized in additional figures for each load profile. The files in this data repository are sorted by a two-digit numerical code indicating the economy division according to NACE Rev. 2 [1] (see README) and a one-digit numerical code indicating the detected clusters. The dependency of natural gas consumption on working days on mean daily ambient temperature increases from cluster 0 to cluster 3. The cluster CHP includes consumers that were excluded from the analysis, since they operate a combined heat and power plant (CHP). In a plausibility check, additional consumers were excluded from the analysis. These consumers are assigned to the cluster -1. References [1] Eurostat. NACE Rev.2: Statistical classification of economic activities in the European Community. Luxembourg: Office for Official Publications of the European Communities; 2008.

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Categories

Energy Engineering, Renewable Energy

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