Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques

Mikel Lumbreras; Roberto Garay; Antonio Garrido Marijuan

doi:10.1088/1755-1315/588/3/032007

Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques

Mikel Lumbreras
, Roberto Garay
, Antonio Garrido Marijuan

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

8 Downloads (Pure)

Abstract

The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final users´ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating & Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R2 values above 0.9 in some of them.

Original language	English
Article number	032007
Pages (from-to)	106-110
Number of pages	5
Journal	IOP Conference Series: Earth and Environmental Science
Volume	588
Issue number	3
DOIs	https://doi.org/10.1088/1755-1315/588/3/032007
Publication status	Published - 20 Nov 2020
Event	World Sustainable Built Environment - Beyond 2020, WSBE 2020 - Gothenburg, Sweden Duration: 2 Nov 2020 → 4 Nov 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Smart energy meters
Energy
Building

Project and Funding Information

Project ID
info:eu-repo/grantAgreement/EC/H2020/768567/EU/REnewable Low TEmperature District/RELaTED
Funding Info
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768567.

Access to Document

10.1088/1755-1315/588/3/032007

Lumbreras 2020 IOP ConfFinal published version, 753 KB

Cite this

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title = "Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques",

abstract = "The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final users´ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating \& Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R2 values above 0.9 in some of them.",

keywords = "Smart energy meters, Energy, Building, Smart energy meters, Energy, Building",

author = "Mikel Lumbreras and Roberto Garay and Marijuan, \{Antonio Garrido\}",

note = "Publisher Copyright: {\textcopyright} Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.; World Sustainable Built Environment - Beyond 2020, WSBE 2020 ; Conference date: 02-11-2020 Through 04-11-2020",

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language = "English",

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Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques. / Lumbreras, Mikel; Garay, Roberto; Marijuan, Antonio Garrido.
In: IOP Conference Series: Earth and Environmental Science, Vol. 588, No. 3, 032007, 20.11.2020, p. 106-110.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques

AU - Lumbreras, Mikel

AU - Garay, Roberto

AU - Marijuan, Antonio Garrido

N1 - Publisher Copyright: © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd.

PY - 2020/11/20

Y1 - 2020/11/20

N2 - The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final users´ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating & Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R2 values above 0.9 in some of them.

AB - The use of smart energy meters enables the monitoring of large quantity of data related to heat consumption patterns in buildings connected to DH networks. This information can be used to understand the interaction between building and the final users´ without accurate information about building characteristics and occupational rates. In this paper an intuitive and clarifier data-driven model is presented, which couples heat demand and weather variables. This model enables the disaggregation of Space-Heating & Domestic Hot water demand, characterization of the total heat demand and the forecasting for the next hours. Simulations for 53 building have been carried out, with satisfactory results for most of them, reaching R2 values above 0.9 in some of them.

KW - Smart energy meters

KW - Energy

KW - Building

KW - Smart energy meters

KW - Energy

KW - Building

UR - https://www.scopus.com/pages/publications/85097760588

U2 - 10.1088/1755-1315/588/3/032007

DO - 10.1088/1755-1315/588/3/032007

M3 - Conference article

SN - 1755-1307

VL - 588

SP - 106

EP - 110

JO - IOP Conference Series: Earth and Environmental Science

JF - IOP Conference Series: Earth and Environmental Science

IS - 3

M1 - 032007

T2 - World Sustainable Built Environment - Beyond 2020, WSBE 2020

Y2 - 2 November 2020 through 4 November 2020

ER -

Energy meters in District-Heating Substations for Heat Consumption Characterization and Prediction Using Machine-Learning Techniques

Abstract

UN SDGs

Keywords

Project and Funding Information

Access to Document

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