Heat Loss Coefficient

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Noureddine Moummi - One of the best experts on this subject based on the ideXlab platform.

Dirk Saelens - One of the best experts on this subject based on the ideXlab platform.

  • 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, 2020
    Co-Authors: Marieline Senave, Stijn Verbeke, Staf Roels, Dirk Saelens
    Abstract:

    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.

  • assessment of data analysis methods to identify the Heat Loss Coefficient from on board monitoring data
    Energy and Buildings, 2020
    Co-Authors: Marieline Senave, Glenn Reynders, Stijn Verbeke, Staf Roels, Dirk Saelens
    Abstract:

    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.

  • Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis
    Energies, 2019
    Co-Authors: Marieline Senave, Stijn Verbeke, Staf Roels, Evi Lambie, Dirk Saelens
    Abstract:

    Recently, there has been an increasing interest in the development of an approach to characterize the as-built Heat Loss Coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar Heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

  • mapping the pitfalls in the characterisation of the Heat Loss Coefficient from on board monitoring data using arx models
    Energy and Buildings, 2019
    Co-Authors: Marieline Senave, Glenn Reynders, Behzad Sodagar, Stijn Verbeke, Dirk Saelens
    Abstract:

    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%.

  • towards the characterization of the Heat Loss Coefficient via on board monitoring physical interpretation of arx model Coefficients
    Energy and Buildings, 2019
    Co-Authors: Marieline Senave, Glenn Reynders, Stijn Verbeke, Staf Roels, Peder Bacher, Dirk Saelens
    Abstract:

    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%.

Kewei Cheng - One of the best experts on this subject based on the ideXlab platform.

  • 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, 2016
    Co-Authors: Hao Li, Xinyu Zhang, Xindong Tang, Kewei Cheng
    Abstract:

    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, 2015
    Co-Authors: Hao Li, Xinyu Zhang, Kewei Cheng
    Abstract:

    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, 2015
    Co-Authors: Hao Li, Xinyu Zhang, Kewei Cheng
    Abstract:

    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).

Chawki Mahboub - One of the best experts on this subject based on the ideXlab platform.

Marieline Senave - One of the best experts on this subject based on the ideXlab platform.

  • 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, 2020
    Co-Authors: Marieline Senave, Stijn Verbeke, Staf Roels, Dirk Saelens
    Abstract:

    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.

  • assessment of data analysis methods to identify the Heat Loss Coefficient from on board monitoring data
    Energy and Buildings, 2020
    Co-Authors: Marieline Senave, Glenn Reynders, Stijn Verbeke, Staf Roels, Dirk Saelens
    Abstract:

    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.

  • Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis
    Energies, 2019
    Co-Authors: Marieline Senave, Stijn Verbeke, Staf Roels, Evi Lambie, Dirk Saelens
    Abstract:

    Recently, there has been an increasing interest in the development of an approach to characterize the as-built Heat Loss Coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar Heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

  • mapping the pitfalls in the characterisation of the Heat Loss Coefficient from on board monitoring data using arx models
    Energy and Buildings, 2019
    Co-Authors: Marieline Senave, Glenn Reynders, Behzad Sodagar, Stijn Verbeke, Dirk Saelens
    Abstract:

    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%.

  • towards the characterization of the Heat Loss Coefficient via on board monitoring physical interpretation of arx model Coefficients
    Energy and Buildings, 2019
    Co-Authors: Marieline Senave, Glenn Reynders, Stijn Verbeke, Staf Roels, Peder Bacher, Dirk Saelens
    Abstract:

    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%.

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