Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach
This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from...
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| Vydané v: | Energy (Oxford) Ročník 291; s. 130351 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.03.2024
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| ISSN: | 0360-5442, 1873-6785 |
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| Abstract | This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation.
[Display omitted]
•Calculation of space heating (SH) and domestic hot water (DHW) from total energy use.•Requires only hourly data from smart heat and water meters; is computationally fast.•Developed using one year’s data from over 2400 single-family houses.•Method validated against ground truth data from 3 single family houses.•Accuracy estimate can be obtained without labelled known data. |
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| AbstractList | This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation. This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4 s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation. This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation. [Display omitted] •Calculation of space heating (SH) and domestic hot water (DHW) from total energy use.•Requires only hourly data from smart heat and water meters; is computationally fast.•Developed using one year’s data from over 2400 single-family houses.•Method validated against ground truth data from 3 single family houses.•Accuracy estimate can be obtained without labelled known data. |
| ArticleNumber | 130351 |
| Author | Widén, Joakim Marszal-Pomianowska, Anna Schaffer, Markus Larsen, Tine Steen Vera-Valdés, J. Eduardo |
| Author_xml | – sequence: 1 givenname: Markus orcidid: 0000-0003-3972-413X surname: Schaffer fullname: Schaffer, Markus email: msch@build.aau.dk organization: Department of the Built Environment, Aalborg University, Aalborg, 9220, Denmark – sequence: 2 givenname: Joakim surname: Widén fullname: Widén, Joakim organization: Built Environment Energy Systems Group, Department of Civil and Industrial Engineering, Uppsala University, Uppsala, 752 37, Sweden – sequence: 3 givenname: J. Eduardo orcidid: 0000-0002-0337-8055 surname: Vera-Valdés fullname: Vera-Valdés, J. Eduardo organization: Department of Mathematical Sciences, Aalborg University, Aalborg, 9220, Denmark – sequence: 4 givenname: Anna orcidid: 0000-0002-3195-7388 surname: Marszal-Pomianowska fullname: Marszal-Pomianowska, Anna organization: Department of the Built Environment, Aalborg University, Aalborg, 9220, Denmark – sequence: 5 givenname: Tine Steen orcidid: 0000-0002-4704-6503 surname: Larsen fullname: Larsen, Tine Steen organization: Department of the Built Environment, Aalborg University, Aalborg, 9220, Denmark |
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| Keywords | Data-driven algorithms Domestic hot water Smart heat meter District heating Space heating Smart water meter |
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| SubjectTerms | algorithms Data-driven algorithms District heating Domestic hot water energy heat regression analysis Smart heat meter Smart water meter Space heating |
| Title | Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach |
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