1. Background
The rise of living standards, the scarcity of natural resources and the awareness of climate change resulted in an international pressure to significantly reduce the energy consumption of buildings and communities. In several countries more stringent requirements are imposed by energy performance legislation and also an increased awareness for environmental issues in building codes can be noticed. Mostly, requirements and labelling of the energy performances of buildings is done in the design phase by calculating the theoretical energy consumption. Several studies showed however that the real performance after realisation of the building may deviate significantly from this theoretically designed performance. Part of the deviations can be attributed to the user behaviour (one of the key topics of research within IEA ECBCS Annex 53), but the main part has to be attributed to the physical features of the building and its systems. For the latter, building performance characterisation based on full scale dynamic measurements could help to bridge the gap between theoretically predicted and real life performance of buildings. Full scale testing is e.g. helpful to investigate the performances of building components and whole buildings as built in reality, including the influence of workmanship.
Examples as those mentioned above, explain why at present several in situ testing activities are going on. A recent international workshop showed the interest for full scale testing from all over the world [BBRI, 2011]. A growing activity is observed in both full scale testing on building components (as e.g. in Paslink-cells or in situ on components of real buildings) and on whole buildings (to characterise thermal performance and energy efficiency of either test buildings or real buildings). So it is clear that, contrary to what was expected, the numerical building component and building energy simulation models did not make full scale testing of building (components) redundant. On the contrary, together with an increased application of numerical simulations, a renewed interest in full scale testing can be observed. This is not so strange, because dynamic full scale testing showed not only to be of interest to study building (component) performances under different real conditions – and as illustrated, quite often a huge difference is observed between predicted and realised performances –, it is also a valuable and necessary tool to integrate advanced components and systems into simulation models. Based on the dynamic data analysis of the measurement results a so-called grey box-model can be deduced [Lodi et al., 2011]. A grey box model is based on a combination of prior physical knowledge and statistics by identifying the unknown parameters of the system with dynamic data analysis. Once identified, the grey box model is able to predict the thermal dynamic response of ventilated photovoltaic double skin facades under different real dynamic climatic conditions. This way it can be ensured that the behaviour of new advanced building components is integrated in a correct way in building energy simulation (BES) models.
A similar approach of parameter identification based on dynamic measurements, can be used to identify suitable models to describe the thermal dynamics of whole buildings including building systems [Bacher and Madsen, 2011]. Characterising the dynamic behaviour of buildings is an essential and very valuable input e.g. when optimising energy grids for building communities.
But, notwithstanding the renewed interest in full scale testing, practice shows that the outcome of many on site activities can be questioned in terms of accuracy and reliability. The focus of nearly all full scale testing activities is on the assessment of the components and buildings, often neglecting the necessity of reliable assessment methods and quality assurance issues. Full scale testing however, requires quality on all topics of the process chain, starting with a good test infrastructure. Only when this is present a good experimental set-up can be designed, which produces reliable data that can be used for dynamic data analysis to come to a characterisation and final use of the results. As soon as the required quality fails on one of the topics, the results become inconclusive or might even be wrong. Therefore, it seems useful to create an international collaboration in the context of IEA ECBCS to develop common quality procedures for full scale testing and data analysis to come to a reliable performance characterisation and prediction of building components and whole buildings.
2. Objectives
The previous section showed that a better characterisation and prediction of the real building performances is essential to realise the world wide intended energy reduction in building communities and systems. Quantifying the real performances of buildings, verifying our calculation models and integrating new advanced energy solutions for nearly zero energy or positive energy buildings can only be effectively realised by in situ testing and dynamic data analysis. Previous research projects [Strachan and Baker, 2008] showed that successful on site measurements and corresponding data analysis require advanced skills in different fields including:
- the setting up of a good test environment (test cells or real buildings, accuracy of sensors and correct installation, data acquisition software,…)
- a good experimental set-up (e.g. test lay-out, imposed boundary conditions for testing,…)
- a good data analysis based on advanced statistical methods in order to come to reliable accuracy intervals
- appropriate methods for scaling and replication of the measured performances.
Though, aiming to quantify the real performance of building components and buildings, many current on-site activities nowadays are not achieving the required quality in one or more of the above mentioned areas, with as result that the outcome and results are from a scientific point of view inconclusive. In the light of the importance of real building performance characterisation, the current research proposal has two main objectives:
- Develop common quality procedures for dynamic full scale testing to come to a better performance analysis
- Develop models to characterise and predict the effective (hygro-)thermal performances of building components and whole buildings.
The ultimate goal of the Annex is hence to
develop the necessary knowledge, tools and networks to achieve reliable in situ dynamic testing and data analysis methods that can be used to characterise the actual energy performance of building components and whole buildings.
To reach this goal, an international collaboration is needed on different issues: development of quality procedures for full scale testing, development of quality procedures for dynamic data analysis, guidelines for building performance characterisation and predictions, gathering well documented high quality dynamic data for validation purposes, applications of dynamic whole building test data,… At the same time it seems interesting to create an operational network of excellence on full scale testing that can provide advice on the whole process and specific on the dynamic data analysis handling.
3. Means
To reach the final goal and different objectives of the proposed project it is necessary to keep in mind that successful full scale dynamic testing requires quality over the whole process chain: a good test infrastructure, the setting up of a good experimental set-up, a reliable dynamic data analysis and appropriate use of the results. Therefore, the research project is organised around this process chain as illustrated in Figure 4 and the following subtasks are defined:
Subtask 1: State of the art on full scale testing and dynamic data analysis
Subtask 2: Optimising full scale dynamic testing
Subtask 3: Dynamic data analysis and performance characterisation
Subtask 4: Application of the developed framework
Subtask 5: Setting up a Network of excellence
Subtask 1. State of the art on full scale testing and dynamic data analysis
Subtask 1 is a short introductory subtask. Based on a literature review and existing reports an overview and evaluation is made of previous and ongoing in situ test activities. An inventory will be made of full scale test facilities available at different institutes all over the world and the common methods with their advantages and drawbacks to analyse the dynamic data will be described. This allows to give an overview of the current state of the art on full scale testing and dynamic data analysis and to highlight the necessary skills within the different knowledge fields to manage the whole chain of activities related to on site test activities.
Subtask 2. Optimising full scale dynamic testing
Subtask 2 establishes the procedure how to realise a good test environment and test set-up. Aim is to come to a roadmap on how to measure the actual thermal performance of building components and whole buildings in situ. This means under realistic boundary conditions (field exposure or artificial climate) and taking into account workmanship. Subtask 2 will consist of the following tasks:
ST2.1. Choice of infrastructure and optimising experimental design
ST2.1 will focus both on the requirements of a good test environment as well as on the setting up of good full scale tests. Topics to be included are the quality of the test environment, measurement sensors (types, number of sensors, positioning, calibration), monitoring systems, possible performance disturbance of sensors, controls during measurements,... Small common exercises will be set up to investigate the different issues.
A specific topic of attention is the use of numerical models for the design of good full scale testing. How can simulations help to investigate the influence of the experimental conditions, to optimise the set-up, the positioning of the sensors, the frequency and duration of measurements,...
ST2.2. Handling and documenting data sets
Full scale dynamic testing results in a large amount of data. Since full scale testing is expensive, a good handling and documenting of data sets is crucial, certainly when the data should be of use for other people. In ST2.2 guidelines on how to perform data handling and documenting of data sets are put forward in a way that no information on the obtained data gets lost.
In the framework of Subtask 2 also some in situ measured data sets will be collected. Several examples of in situ measured data sets can be found in literature, but in most cases the measurements are only intended to illustrate certain issues and lack generality. Due to the absence of details and documentation, the data is often of limited value to other researchers in the field. To develop reliable procedures for dynamic data analysis and to perform validation of BES-models on real measured data, well documented data sets of in situ dynamic testing are needed. In the framework of Subtask 2 a few data sets on the different scales (building components, whole buildings, buildings including systems) will be collected. The number of sets will be limited, but the data sets should be of high quality and very well documented.
In addition, to check the robustness of in situ measuring, a round robin experiment on dynamic in situ testing will be performed. A scale model of a building (a box-model comparable with the BESTEST-model for validation of BES-models) will be built and sent around to different institutes all over the world to perform dynamic measurements of the same box under different climatic conditions.
The obtained data will be used in Subtask 3 to develop and validate different dynamic data analysis procedures and in Subtask 4 to verify the common BES-models.
Subtask 3. Dynamic data analysis and performance characterisation
Subtask 3 focuses on quality procedures for full scale dynamic data analysis and on how to characterise building components and whole buildings starting from full scale dynamic data tests. Subtask 3 hence contains two major tasks:
ST3.1. Development of procedures for high quality dynamic data analysis
Analysing the measured data of in situ testing requires dynamic analysis methods and models. A wide range of methodologies exist, and it is often not easy to choose the most appropriate approach for each particular case. ST3.1 will focus on which methodology to use for dynamic data analysis, taking into account the purpose of the in situ testing, the existence of prior physical knowledge, the available data and the statistical tools,... The methodologies will be tested and validated on the data collected in ST2. That way quality procedures and guidelines for dynamic data analysis can be developed.
ST3.2. Determination of reliable performance indicators for actual thermal performance of buildings
Focus of ST3.2 is on the determination of reliable performance indicators for the actual thermal performance of building components and whole buildings. Questions rise for instance whether static performance indicators as a constant g-value or U-value are still useful when characterizing highly insulated nearly zero energy buildings and whether the focus should not move towards dynamic performance indicators.
Subtask 4. Application of the developed framework
Subtask 4 will apply the developed concepts and show the applicability and importance of full scale dynamic testing for different issues with respect to energy conservation in buildings and community systems.
ST4.1. Verification of common BES-models based on in situ dynamic data
Verification and validation of numerical BES-codes requires well-documented high quality data sets. These will be gathered in ST2 and will be confronted with numerical simulations in this subtask. This is a very important addition to the existing BESTEST and several validation standards [ref. EN ISO 13792, prEN 15255, prEN 15265] , which only performed an intermodel comparison. Both the small scale model from the round robin test as well as full scale in situ data from ST2 will be used for the validation test.
ST4.2. Towards a characterisation of buildings based on in situ testing and smart meter readings
Nowadays several new advanced techniques such as home automation, smart meter readings,.. keep track of a lot of information about the buildings response, the energy consumption, the in- and outdoor conditions,… ST4.2 will investigate if it is possible to characterise the thermal performance of buildings by using this information together with dynamic data analysis (e.g. by filtering out the user behaviour) and without (too much) extra in situ testing. Final aim is to develop this way a real energy performance characterisation of a building based on on-site gathered information.
ST4.3. Application of dynamic building characterisation for optimising smart grids
The evolution towards nearly zero energy buildings changes the way the buildings interact with energy distribution grids. Buildings no longer only use energy, but also act as distributed renewable energy sources. As a result, optimising smart energy grids becomes a crucial issue. Currently, buildings are often implemented as simplified models in the optimisation process. However, a characterisation of the dynamic behaviour of buildings is necessary to design and operate smart grids in a reliable way. The aim of ST4.3 is to identify suitable models for describing the energy dynamics of buildings. They can be used to optimise e.g. smart grids, smart meters in residential applications and controllers implemented in building energy management systems for larger buildings.
Subtask 5. Setting up a Network of excellence
Previous and current networks such as PASLINK and DYNASTEE have shown the relevance of a network of excellence for knowledge exchange and guidelines on testing. Within this IEA ECBCS Annex-project a network will be set up on 'in situ testing and dynamic data analysis' with a similar goal. As mentioned before, full scale dynamic testing will only produce conclusive results when the quality is guaranteed in all stages of the process. Since the different stages (test infrastructure, experimental set-up, data analysis and use of results) are often in different hands, a network of excellence can advise organisations interested in testing campaigns on the whole process of in situ testing. Due to its specific features, the dynamic data analysis will be a specific point of attention within the whole process. To achieve that the network survives when the Annex-project is finalized, continuity in an organized international framework is aimed at.
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