Theoretical and experimental investigation of startup and shutdown behavior of residential heat pumps

Uhlmann, Michael; Bertsch, Stefan S.

doi:10.1016/j.ijrefrig.2012.08.008

Cited by 42 publications

(7 citation statements)

References 9 publications

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“…The dynamic behaviour of the HP is studied by [45][46][47][48]. The major differences to the condensing boiler are variations in the start-up and shut-down phases, which are neglected in this experimental case study to justify the usage of a virtual HP model.…”

Section: Methodsmentioning

confidence: 99%

Economic model predictive control for demand flexibility of a residential building

Finck

Zeiler

2019

Energy

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Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy.In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from -0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

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Section: Methodsmentioning

confidence: 99%

Economic model predictive control for demand flexibility of a residential building

Finck

Zeiler

2019

Energy

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“…The modification of the wave frequency and amplitude is indicative of the properties of the fluid/gas. There exist several methods that can be used to calculate the final flow rate, for example, based on the time of transit of the sonic waves [ 36 ], the Doppler Effect [ 37 ], cross-correlation [ 37 ] or changes in wave amplitude [ 38 ]. Ultrasonic sensors are used in many areas of industry.…”

Section: Sensor Technologiesmentioning

confidence: 99%

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Prauzek,

Kucova,

Konecny

et al. 2023

Sensors

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Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

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“…The model allowed the estimation of electricity consumption to drive the compressor with assumed electrical and mechanical efficiencies. Uhlmann and Bertsch [11] developed a model for a compressor heat pump based on the analysis of heat fluxes on the low-and highpressure side, taking into account the mechanical power and electrical energy required to drive the compressor. The model developed was valid for a certain range of input parameters-evaporating temperature and pressure-and was based on the model described by Klein, for which the EES (Engineering Equation Solver) software was used to solve.…”

Section: Most Commonly Used Compressor Heat Pump Modelsmentioning

confidence: 99%

“…The model developed was valid for a certain range of input parameters-evaporating temperature and pressure-and was based on the model described by Klein, for which the EES (Engineering Equation Solver) software was used to solve. The advantage of the model presented was that simulation studies could be carried out for different refrigerants [11]. A similar approach to modelling the operation of a compressor heat pump was presented in [12].…”

Section: Most Commonly Used Compressor Heat Pump Modelsmentioning

confidence: 99%

Compressor Heat Pump Model Based on Refrigerant Enthalpy and Flow Rate

Obstawski,

Bakoń,

Gajkowski

2023

Adv. Sci. Technol. Res. J.

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This paper introduces a compressor heat pump model using flow rate and enthalpy values for the calculation of heat pump control parameters. The aim of in this article presented research work was to develop an universal model of a compressor heat pump which will enable simulation tests for compressors of any volumetric efficiency with variable operating parameters (e.g. temperature of the lower source and condensation temperature) worked with any refrigerant. An additional assumption was the possibility of conducting simulation tests continuously. The model was developed on the basis of equations describing the thermodynamic transformations taking place in the refrigeration system. The data needed for the model are the input signals of saturation and condensation temperatures and the refrigerant mass flow rate entered as a time series. The output signals are heating capacity, cooling capacity, power consumed by the compressor, and the temperature at the second point of the thermodynamic cycle of the refrigerant. The paper presents equations for examples of the frequently used refrigerants R410a and R290 and practical verification of the presented algorithm in the laboratory made for refrigerant R410a. A description of the laboratory stand is also included. Comparison of the capacity provided by the condenser and the coefficient of performance with the values simulated using the proposed model confirms the correctness of the applied model and its practicality. The results obtained by simulation and measurements show very good convergence. The developed model makes it possible to calculate the operational parameters of the device, e.g. such as COP and SCOP for given boundary conditions, which then enables to estimate the coverage degree of the heat load of the building by the heat pump due to central heating and to estimate momentary and seasonal operating costs of the heat pump.

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Theoretical and experimental investigation of startup and shutdown behavior of residential heat pumps

Cited by 42 publications

References 9 publications

Economic model predictive control for demand flexibility of a residential building

Economic model predictive control for demand flexibility of a residential building

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Compressor Heat Pump Model Based on Refrigerant Enthalpy and Flow Rate

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