Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

Mikel Lumbreras, Roberto Garay-Martinez, Beñat Arregi, Koldobika Martin-Escudero, Gonzalo Diarce, Margus Raud and Indrek Hagu

Energy, 2022, vol. 239, issue PD

Abstract: An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network.

Keywords: Load forecasting; Heat meters; Data-driven model; Building; District Heating (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221025664
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221025664

DOI: 10.1016/j.energy.2021.122318

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().