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Marchante-Avellaneda, J., Navarro-Peris, E. & Song, Y. (2024). Development of map-based models for the performance characterization in a new prototype of Dual Source Heat Pump. Applied Thermal Engineering, 236, Article ID 121743.
Open this publication in new window or tab >>Development of map-based models for the performance characterization in a new prototype of Dual Source Heat Pump
2024 (English)In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 236, article id 121743Article in journal (Refereed) Published
Abstract [en]

This paper presents the development of accurate map-based models for characterizing a new Dual Source Heat Pump prototype. This unit includes three braze plate heat exchangers and a round tube fin heat exchanger, allowing the unit to select different operating modes such as heat pump, chiller, and domestic hot water production using as source the ground or air. Therefore, due to the hybrid typology of this unit and the possibility of reversing the cycle, this work covers the main heat pump and refrigeration equipment technologies currently available on the market (air source and ground source units). The modeling strategy selected has been to provide several polynomial expressions to predict the performance of these units, i.e., compressor energy consumption and condenser and evaporator capacities. This approach allows obtaining accurate, compact, and easy-to-implement models for developing dynamic models of more complex systems where this type of unit – the heat pump – is an integrated part of the system. Currently, a clear example of this modeling strategy can be found in characterizing one of the main components installed in these machines, the compressor. The AHRI-540 standard specifies a polynomial model as a function of evaporating and condensing temperatures. In this sense, for the characterization of heat pumps, the polynomials developed depend only on the unit's external variables, so they can be useful in many scenarios, obtaining direct feedback on the heat pump performance when developing a dynamic model to optimize system control strategies or to develop techno-economic studies. In this case, the hybrid typology of this unit makes it particularly relevant to optimize the control to manage the type of source to be used (air or ground), allowing the development of a more efficient and sustainable technology by selecting the most adequate source in terms of performance. This study focuses on obtaining the polynomial expressions that minimize the number of terms while simultaneously minimizing prediction error. By carefully selecting the most significant terms and suitable transformations in the characterized variables, the goal is to prevent overfitting, minimize potential extrapolation or interpolation errors and obtain polynomial expressions that can be fitted with small experimental samples. For this purpose, a detailed model implemented in a commercial software for heat pump characterization has been used, with which a large number of simulation results were generated. These simulation results include a fine meshing working map of the unit that allowed us to analyze the relationships between the characterized and external variables.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Air source heat pump, Dual source heat pump, Ground source heat pump, Heat pump performance, Polynomial models
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-338875 (URN)10.1016/j.applthermaleng.2023.121743 (DOI)2-s2.0-85173961965 (Scopus ID)
Note

QC 20231031

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-11-21Bibliographically approved
Song, Y., Rolando, D., Avellaneda, J. M., Zucker, G. & Madani Larijani, H. (2023). Data-driven soft sensors targeting heat pump systems. Energy Conversion and Management, 279, 116769, Article ID 116769.
Open this publication in new window or tab >>Data-driven soft sensors targeting heat pump systems
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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
Keywords
Data driven, Heat pumps, Soft sensors, ANN, Regression, Database
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-324638 (URN)10.1016/j.enconman.2023.116769 (DOI)000933059100001 ()2-s2.0-85147371380 (Scopus ID)
Note

QC 20230309

Available from: 2023-03-09 Created: 2023-03-09 Last updated: 2023-03-09Bibliographically approved
Song, Y., Peskova, M., Rolando, D., Zucker, G. & Madani Larijani, H. (2023). Estimating electric power consumption of in-situ residential heat pump systems: A data-driven approach. Applied Energy, 352, Article ID 121971.
Open this publication in new window or tab >>Estimating electric power consumption of in-situ residential heat pump systems: A data-driven approach
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2023 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 352, article id 121971Article in journal (Refereed) Published
Abstract [en]

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R2, 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R2, 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Data driven, Electric power, Heat pump, Heating, Machine learning, Regression model
National Category
Energy Engineering
Identifiers
urn:nbn:se:kth:diva-338357 (URN)10.1016/j.apenergy.2023.121971 (DOI)001086100200001 ()2-s2.0-85172678028 (Scopus ID)
Note

QC 20231115

Available from: 2023-10-20 Created: 2023-10-20 Last updated: 2023-11-15Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1187-7065

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