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Data-driven soft sensors targeting heat pump systems
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0003-4387-806x
Univ Politecn Valencia, Inst Univ Invest Ingn Energet, Camino Vera S-N,Ed 8E Semisotano, Valencia 46022, Spain..
Austrian Inst Technol, Sustainable Thermal Energy Syst, Giefinggasse 2, A-1210 Vienna, Austria..
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2023 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 279, p. 116769-, article id 116769Article in journal (Refereed) Published
Abstract [en]

The development of smart sensors, low cost communication, and computation technologies enables continuous monitoring and accumulation of tremendous amounts of data for heat pump systems. But the measurements, especially for domestic heat pump, usually suffer from incompleteness given technical and/or economic barriers, which prevents database of measurements from being exploited to its full potential. To this end, this work proposes a data-driven soft sensor approach for compensating multiple missing information. The soft sensors are developed based on an ANN model, an integrated multivariate polynomial regression model and empirical model by considering different constrains like data and information availability during model establishing process. All the three models have been validated against the data from a field test installation, and showed good performance for all the compensated variables. Of the three models, the ANN model shows the best performance for all soft sensors, but it has the highest requirement for additional resources to collect training data. While the integrated multivariate polynomial regression model demonstrates excellent accuracy for the majority of soft sensors with manufacturers' subcomponent data which needs no extra cost. Even though empirical model is not as accurate as the other two models, it still performs good accuracy with limited information from performance map. The methods developed in the present study paves the way for available measured data in thousands of installations to be fully utilized for innovative services including but not limited to: improved heat pump control strategies, fault detection and diagnosis, and communication with local energy grids.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 279, p. 116769-, article id 116769
Keywords [en]
Data driven, Heat pumps, Soft sensors, ANN, Regression, Database
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-324638DOI: 10.1016/j.enconman.2023.116769ISI: 000933059100001Scopus ID: 2-s2.0-85147371380OAI: oai:DiVA.org:kth-324638DiVA, id: diva2:1742395
Note

QC 20230309

Available from: 2023-03-09 Created: 2023-03-09 Last updated: 2023-03-09Bibliographically approved

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Song, YangRolando, DavideMadani Larijani, Hatef

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