On the surplus accuracy of data-driven energy quantification methods in the residential sector
Abstract Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-06-01
|
| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-022-00194-8 |
| _version_ | 1818205313854078976 |
|---|---|
| author | Lars Wederhake Simon Wenninger Christian Wiethe Gilbert Fridgen |
| author_facet | Lars Wederhake Simon Wenninger Christian Wiethe Gilbert Fridgen |
| author_sort | Lars Wederhake |
| collection | DOAJ |
| description | Abstract Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods. |
| first_indexed | 2024-12-12T03:55:09Z |
| format | Article |
| id | doaj.art-7cb83c2687724bf695d5ba23d2800b60 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-12-12T03:55:09Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-7cb83c2687724bf695d5ba23d2800b602022-12-22T00:39:16ZengSpringerOpenEnergy Informatics2520-89422022-06-015112410.1186/s42162-022-00194-8On the surplus accuracy of data-driven energy quantification methods in the residential sectorLars Wederhake0Simon Wenninger1Christian Wiethe2Gilbert Fridgen3credium GmbHBranch Business & Information Systems Engineering of the Fraunhofer FITBranch Business & Information Systems Engineering of the Fraunhofer FITSnT–Interdisciplinary Center for Security, Reliability and Trust, University of LuxembourgAbstract Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods.https://doi.org/10.1186/s42162-022-00194-8Energy quantification methodsData-driven methodsBuilding energy dataData qualityBuilding energy performancePrediction accuracy theory |
| spellingShingle | Lars Wederhake Simon Wenninger Christian Wiethe Gilbert Fridgen On the surplus accuracy of data-driven energy quantification methods in the residential sector Energy Informatics Energy quantification methods Data-driven methods Building energy data Data quality Building energy performance Prediction accuracy theory |
| title | On the surplus accuracy of data-driven energy quantification methods in the residential sector |
| title_full | On the surplus accuracy of data-driven energy quantification methods in the residential sector |
| title_fullStr | On the surplus accuracy of data-driven energy quantification methods in the residential sector |
| title_full_unstemmed | On the surplus accuracy of data-driven energy quantification methods in the residential sector |
| title_short | On the surplus accuracy of data-driven energy quantification methods in the residential sector |
| title_sort | on the surplus accuracy of data driven energy quantification methods in the residential sector |
| topic | Energy quantification methods Data-driven methods Building energy data Data quality Building energy performance Prediction accuracy theory |
| url | https://doi.org/10.1186/s42162-022-00194-8 |
| work_keys_str_mv | AT larswederhake onthesurplusaccuracyofdatadrivenenergyquantificationmethodsintheresidentialsector AT simonwenninger onthesurplusaccuracyofdatadrivenenergyquantificationmethodsintheresidentialsector AT christianwiethe onthesurplusaccuracyofdatadrivenenergyquantificationmethodsintheresidentialsector AT gilbertfridgen onthesurplusaccuracyofdatadrivenenergyquantificationmethodsintheresidentialsector |