The Experts below are selected from a list of 15561 Experts worldwide ranked by ideXlab platform
Kewei Cheng - One of the best experts on this subject based on the ideXlab platform.
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extreme learning machine a new alternative for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
SpringerPlus, 2016Co-Authors: Hao Li, Xinyu Zhang, Xindong Tang, Kewei ChengAbstract:Background Heat collection rate and heat Loss Coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower.
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artificial neural networks based software for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
PLOS ONE, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:Measurements of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat Loss Coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat Loss Coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat Loss Coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
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novel method for measuring the heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters based on artificial neural networks and support vector machine
Energies, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:The determinations of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat Loss Coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat Loss Coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat Loss Coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).
Subhash C Mullick - One of the best experts on this subject based on the ideXlab platform.
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glass cover temperature and top heat Loss Coefficient of a single glazed flat plate collector with nearly vertical configuration
Ain Shams Engineering Journal, 2012Co-Authors: Suresh Kumar, Subhash C MullickAbstract:Abstract An empirical relation for glass cover temperature of a single glazed flat plate collector for angle of tilt 60–90° is proposed. Values of glass cover temperature obtained from empirical relation have been used for computation of top heat Loss Coefficient of collector. Analytical equation has been employed for estimation of top heat Loss Coefficient, U t . The range of variables covered in the present analysis is 20 °C to 150 °C for absorber plate temperature, 0.1–0.95 for absorber coating emittance, 20–50 mm for air gap spacing, 60–90° for collector tilt, 5–30 W/m 2 K for wind heat transfer Coefficient and −10 °C to 40 °C for ambient temperature. The maximum absolute error in values of U t is within two percent, in comparison to values obtained by numerical solution of heat balance equations, over the entire range of variables.
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computation of glass cover temperatures and top heat Loss Coefficient of flat plate solar collectors with double glazing
Energy, 2007Co-Authors: Naiem Akhtar, Subhash C MullickAbstract:A set of correlations for computing the glass-cover temperatures of flat-plate solar collectors with double glazing is developed. A semi-analytical correlation for the factor f2—the ratio of outer to inner thermal resistance of a double-glazed collector—as a function of collector parameters and atmospheric variables is obtained by regression analysis. This relation readily provides the temperature of the second (outer) glass cover (T2). For estimating the temperature of first (inner) glass cover (T1), another relation for the factor f1—the ratio of thermal resistance between the two glass covers to the thermal resistance between the absorber plate and first glass cover—is developed. A wide range of variables is covered in the present analysis. The results are compared with those obtained by numerical solutions of heat-balance equations. Using the proposed relations of glass-cover temperatures, the values of top heat Loss Coefficient (Ut) can be computed and are found to be very close to those obtained by numerical solutions of heat-balance equations. The maximum absolute error in the calculation of Ut by the proposed method is only 1.0%, so numerical solutions of heat-balance equations for the computation of Ut are not required.
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approximate method for computation of glass cover temperature and top heat Loss Coefficient of solar collectors with single glazing
Solar Energy, 1999Co-Authors: Naiem Akhtar, Subhash C MullickAbstract:Abstract An improved equation form for computing the glass cover temperature of flat-plate solar collectors with single glazing is developed. A semi-analytical correlation for the factor f—the ratio of inner to outer heat-transfer Coefficients—as a function of collector parameters and atmospheric variables is obtained by regression analysis. This relation readily provides the glass cover temperature (Tg). The results are compared with those obtained by numerical solution of heat balance equations. Computational errors in Tg and hence in the top heat Loss Coefficient (Ut) are reduced by a factor of five or more. With such low errors in computation of Tg and Ut, a numerical solution of heat balance equations is not required. The method is applicable over an extensive range of variables: the error in the computation of Ut is within 2% with the range of air gap spacing 8 mm to 90 mm and the range of ambient temperature 0°C to 45°C. In this extended range of variables the errors due to simplified method based on empirical relations for Ut are substantially higher.
Hao Li - One of the best experts on this subject based on the ideXlab platform.
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extreme learning machine a new alternative for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
SpringerPlus, 2016Co-Authors: Hao Li, Xinyu Zhang, Xindong Tang, Kewei ChengAbstract:Background Heat collection rate and heat Loss Coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower.
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artificial neural networks based software for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
PLOS ONE, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:Measurements of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat Loss Coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat Loss Coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat Loss Coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
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novel method for measuring the heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters based on artificial neural networks and support vector machine
Energies, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:The determinations of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat Loss Coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat Loss Coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat Loss Coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).
Dirk Saelens - One of the best experts on this subject based on the ideXlab platform.
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analysis of the influence of the definition of the interior dwelling temperature on the characterization of the heat Loss Coefficient via on board monitoring
Energy and Buildings, 2020Co-Authors: Marieline Senave, Staf Roels, Stijn Verbeke, Dirk SaelensAbstract:Abstract Over the years, several dedicated on-site measurement tests have been developed to assess the heat Loss Coefficient (HLC) of a building envelope. These tests, and their associated data analysis methods, take the single-zone heat balance as a starting point. This assumption is, however, challenged when the measurement data is no longer collected during dedicated heating experiments, but via on-board monitoring (OBM), as recent studies suggest. OBM is the collection of measurement data of the energy use and interior climate of occupied buildings, via sensors. Under normal, ‘in-use’ conditions, the interior temperature of a building is most probably not identical in the various rooms. Using the single-zone heat balance hence requires the definition of an ‘equivalent homogeneous building temperature’. This paper seeks to determine adequate approaches (sensor setups, default values) to assess this temperature. It specifically investigates the impact a certain approach has on the accuracy with which the HLC can be estimated. Hereto, a sensitivity analysis is performed based on six synthetic OBM data sets, generated from simulation models of residential buildings with different envelope performances and occupancy profiles. The results show how using monitoring data of the living room temperature to represent the equivalent homogeneous building temperature can lead to an underestimation of the HLC by up to 30%. This deviation can be limited to 12%, by adopting a default value suggested in a Dutch standard, and even to 6% by using temperature data collected in each individual room and volume weighting these signals. The HLC of uninsulated buildings with many unheated rooms proves to be the hardest to assess.
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assessment of data analysis methods to identify the heat Loss Coefficient from on board monitoring data
Energy and Buildings, 2020Co-Authors: Marieline Senave, Staf Roels, Glenn Reynders, Stijn Verbeke, Dirk SaelensAbstract:Abstract The past decade has seen the rapid development of sensor technologies. Monitoring data of the interior climate and energy consumption of in-use buildings, so-called on-board monitoring (OBM) data, offers the opportunity to identify as-built energy performance indicators, such as the heat Loss Coefficient (HLC) of the building envelope. To this end, it is important to advance the understanding of the impact of the OBM set-up and the applied data analysis method. This paper uses synthetic OBM data sets, generated from building energy simulations. The level of accuracy achieved with four data analysis methods for characterizing the HLC is investigated. The considered methods are the Average Method, the Energy Signature Method, Linear Regression and ARX modeling. Different cases, representing different building types, are considered in order to gain thorough insight into the physical interpretation of the results. By taking subsets of the original data sets, the sensitivity of the data analysis methods to the availability of specific data is assessed. This theoretical exercise illustrates how, under idealized monitoring circumstances, both linear regression and ARX models can accurately determine the HLC. The latter is able to assess the performance indicator within 5%. However, when subjected to practical limitations regarding the measurement of system inputs, such as unavailable solar or internal heat gains, the characterization results show large variations in accuracy and uncertainty.
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mapping the pitfalls in the characterisation of the heat Loss Coefficient from on board monitoring data using arx models
Energy and Buildings, 2019Co-Authors: Marieline Senave, Glenn Reynders, Stijn Verbeke, Behzad Sodagar, Dirk SaelensAbstract:Abstract Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption…). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat Loss Coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%.
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towards the characterization of the heat Loss Coefficient via on board monitoring physical interpretation of arx model Coefficients
Energy and Buildings, 2019Co-Authors: Marieline Senave, Staf Roels, Glenn Reynders, Stijn Verbeke, Peder Bacher, Dirk SaelensAbstract:Abstract This paper explores the concept of characterizing the as-built Heat Loss Coefficient (HLC) of buildings based on-board monitoring (OBM), via energy consumption and temperature sensors, and time series analysis. It is examined (1) how the Coefficients of different Auto-Regressive with eXogenous inputs (ARX) models can be interpreted and (2) whether these conclusions are sensitive to the building envelope assembly or the applied indoor temperature profile. The paper presents a theoretical case study whereby detailed building energy simulations are used to accurately map the impact of physical phenomena on the characterization process. The simulation models and boundary conditions are composed to focus on the link between the estimated ARX-Coefficients and the physical driving forces for transmission heat Loss to the ground and the exterior environment. The results show how the various ARX model Coefficients are linked to specific components of the HLC (e.g. heat transfer through the walls and roof or through the slab-on-ground floor) and to what extent they are affected by the selection of input variables. By monitoring the ground temperature, the transmission heat Losses can rather accurately be assigned to either the slab-on-ground or the walls and roof. Without this measurement data, the uncertainty on the estimates increases (ranges of the 95% confidence interval of up to 35% of the mean estimate). Modeling the ground heat Losses by a constant intercept term leads to underestimations of the reference HLC of up to 59%, whereas adding heat flux sensors to monitor the transmission heat Losses to the ground to the measurement set-up allows to assess the transmission heat transfer Coefficient to the exterior environment HLCe within 2%.
Xinyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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extreme learning machine a new alternative for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
SpringerPlus, 2016Co-Authors: Hao Li, Xinyu Zhang, Xindong Tang, Kewei ChengAbstract:Background Heat collection rate and heat Loss Coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower.
-
artificial neural networks based software for measuring heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters
PLOS ONE, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:Measurements of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat Loss Coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat Loss Coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat Loss Coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
-
novel method for measuring the heat collection rate and heat Loss Coefficient of water in glass evacuated tube solar water heaters based on artificial neural networks and support vector machine
Energies, 2015Co-Authors: Hao Li, Xinyu Zhang, Kewei ChengAbstract:The determinations of heat collection rate and heat Loss Coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat Loss Coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat Loss Coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat Loss Coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).
