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A D-Vine Copula Quantile Regression Approach for the Prediction of Residential Heating Energy Consumption Based on Historical Data

Title data

Niemierko, Rochus ; Töppel, Jannick ; Tränkler, Timm:
A D-Vine Copula Quantile Regression Approach for the Prediction of Residential Heating Energy Consumption Based on Historical Data.
In: Applied Energy. Vol. 233-234 (January 2019) . - pp. 691-708.
ISSN 1872-9118
DOI: https://doi.org/10.1016/j.apenergy.2018.10.025

Official URL: Volltext

Abstract in another language

Energetic retrofitting of residential buildings is poised to play an important role in the achievement of ambitious global climate targets. A prerequisite for purposeful policy-making and private investments is the accurate prediction of energy consumption. Building energy models are mostly based on engineering methods quantifying theoretical energy consumption. However, a performance gap between predicted and actual consumption has been identified in literature. Data-driven methods using historical data can potentially overcome this issue. The D-vine copula-based quantile regression model used in this study achieved very good fitting results based on a representative data set comprising 25,000 German households. The findings suggest that quantile regression increases transparency by analyzing the entire distribution of heating energy consumption for individual building characteristics. More specifically, the analyses reveal the following exemplary insights. First, for different levels of energy efficiency, the rebound effect exhibits cyclical behavior and significantly varies across quantiles. Second, very energy-conscious and energy-wasteful households are prone to more extreme rebound effects. Third, with regards to the performance gap, heating energy demand of inefficient buildings is systematically underestimated, while it is overestimated for efficient buildings.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Data-Driven Heating Energy Analysis; Energetic Retrofitting; Quantile Regression; DVine Copula; Rebound Effect
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 19 Mar 2019 08:24
Last Modified: 20 Mar 2019 06:18
URI: https://eref.uni-bayreuth.de/id/eprint/48036