Design and experimental analysis of an Integral Collector Storage (ICS) prototype for DHW production

Bilardo, Matteo; Fraisse, Gilles; Pailha, Mickaël; Fabrizio, Enrico

doi:10.1016/j.apenergy.2019.114104

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…In recent years, many research centers and universities have focused their research on nZEBs through the construction of full-scale test facilities, to study the effect of design variables on the performance of buildings or technology solutions in reliable and realistic conditions [9]. Numerous articles that report the results of experimental campaigns conducted on these facilities have supported this research approach [10]. Among the studies that have focused on testing high-performance facilities, the most interesting are certainly those that are used for real-life activities, such as offices or residential use.…”

Section: Test Facilities For Building Performance Researchmentioning

confidence: 99%

Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings

et al. 2021

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In recent years, investigations on advanced technological solutions aiming to achieve high-energy performance in buildings have been carried out by research centers and universities, in accordance with the reduction in buildings’ energy consumption required by European Union. However, even if the research and design of new technological solutions makes it possible to achieve the regulatory objectives, a building’s performance during operation deviates from simulations. To deepen this topic, interesting studies have focused on testing these solutions on full-scale facilities used for real-life activities. In this context, a test facility will be built in the university campus of Politecnico di Torino (Italy). The facility has been designed to be an all-electric nearly Zero Energy Building (nZEB), where heating and cooling demand will be fulfilled by an air-source heat pump and photovoltaic generators will meet the energy demand. In this paper, the facility energy performance is evaluated through a dynamic simulation model. To improve energy self-sufficiency, the integration of lithium-ion batteries in a HVAC system is investigated and their storage size is optimized. Moreover, the facility has been divided into three units equipped with independent electric systems with the aim of estimating the benefits of local energy sharing. The simulation results clarify that the facility meets the expected energy performance, and that it is consistent with a typical European nZEB. The results also demonstrate that the local use of photovoltaic energy can be enhanced thanks to batteries and local energy sharing, achieving a greater independence from the external electrical grid. Furthermore, the analysis of the impact of the local energy sharing makes the case study of particular interest, as it represents a simplified approach to the energy community concept. Thus, the results clarify the academic potential for this facility, in terms of both research and didactic purposes.

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Section: Test Facilities For Building Performance Researchmentioning

confidence: 99%

Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings

et al. 2021

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“…This paper applies the GNN with a one-shot learning method to the fault diagnosis of rolling bearings of rotating machinery devices in an innovative way. CWRU bearing data set is used as the experimental data set, and Siamese network [10,32], matching network [3], prototype network [2], SAE [20] and other traditional one-shot learning neural network models are selected for comparison. It is verified that the method has higher accuracy of fault diagnosis and stronger generalization ability, making its classification accuracy as high as 98.02%.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method for rolling bearings based on graph neural network with one-shot learning

Gao,

Wu,

Liao

et al. 2023

EURASIP J. Adv. Signal Process.

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The manuscript proposes a fault diagnosis method based on graph neural network (GNN) with one-shot learning to effectively diagnose rolling bearings under variable operating conditions. In this proposed method, the convolutional neural network is utilized for feature extraction, reducing loss in the process. Subsequently, GNN applies an adjacency matrix to generate codes for one-shot learning. Experimental verification is conducted using open data from Case Western Reserve University Rolling Bearing Data Center, where four different working conditions with six types of typical faults are selected as input signals. The classification accuracy of the proposed method reaches 98.02%. To further validate its effectiveness, traditional single-learning neural networks such as Siamese, Matching Net, Prototypical Net and (Stacked Auto Encoder) SAE are introduced as comparisons. Simulation results that the proposed method outperforms all chosen methods.

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“…That made the share of renewable energy, globally, reach 10.4%. Nevertheless, 79.6% is produced by conventional exploitation of fossil fuels [5]. Furthermore, to provide domestic hot water (DHW) renewable energy has recently become the centre of attention for its availability and low emission results [6].…”

Section: Introductionmentioning

confidence: 99%

Linear Model of DHW System Using Response Surface Method Approach

2022

Teh. vjesn.

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Using alternative and clean energy resources is considered the most effective method to deal with the environment and energy crises nowadays. In this study a comparative analysis of FPC data for DHW are theoretically and experimentally evaluated to optimise the design parameters. A forced circulation solar heating system using flat-plate collector, is modelled using T*SOL as a new approach, for hot water requirements of a laboratory unit at Szent Istvan University, in Gödöllő, Hungary. The modelling shows 69% solar fraction and 510 kWh annual solar gain. Furthermore, the comparison study was based on these two factors, and the two results matched with 93.12% accuracy. After the modelling, practically, the exergy analysis was conducted to determine and highlight the losses of the system. Since Hungary is considered a cold climate country, it was found that the thermal and optical losses from the collector and the piping system were tremendous. Then all the necessary design parameters were studied to achieve the optimal working points using non-linear correlation response surface method (RSM, which has never been used as an analysis tool in the solar field) at two perspectives (solar fraction and annual yield) for seven factors using R script. The factors were collector surface area, inclination angle, glycol-water ratio, tank capacity, boiler capacity, desired hot water temperature, and volume flow-rate. We identified the most influential single factors using ParetoPlot and each two-factorsꞌ interaction using contour plots. The most influential factors on the solar yield are solar collector surface area, tank capacity, desired hot water temperature and volume flow rate by magnitude effects of +129.7, +125.3, +50.9 and +17.2 kWh per annum, respectively.

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Design and experimental analysis of an Integral Collector Storage (ICS) prototype for DHW production

Cited by 18 publications

References 36 publications

Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings

Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings

A fault diagnosis method for rolling bearings based on graph neural network with one-shot learning

Linear Model of DHW System Using Response Surface Method Approach

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