Modelling of a naturally ventilated BIPV system for building energy simulations

Two major causes of energy yield reduction in PV systems are partial shading and high operating temperatures. Both issues are particularly critical for BIPV systems. The correct assessment of the BIPV contribution to the built environment depends, therefore, on the accurate prediction of PV temperature and on the possibility of simulating shading effects. This paper describes the development of a multi-physics model for a naturally ventilated façade BIPV system within the openIDEAS environment for building and district energy simulations. The PV electrical model used here follows a physics-based approach that takes into account solar intensity and temperature spatial variations within the PV module, enabling the simulation of shading effects both at cell and module level. A detailed thermal model has been developed and coupled to the electrical model to estimate the PV temperature. Four case studies illustrate the importance of temperature and shading effects on the PV power output. The model has been validated using data from an experimental BIPV setup deployed in Belgium. The results indicate that the model is able to predict both the PV surface temperature and the power production, given the correct boundary conditions are applied

Mapping the pitfalls in the characterisation of the heat loss coefficient from on-board monitoring data using ARX models

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%

Thermal performance characterisation using time series data : statistical guidelines: report of Subtask 3.2

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Characterizing the energy flexibility of buildings and districts

The large penetration rate of renewable energy sources leads to challenges in planning and controlling the energy production, transmission, and distribution in power systems. A potential solution is found in a paradigm shift from traditional supply control to demand control. To address such changes, a first step lays in a formal and robust characterization of the energy flexibility on the demand side. The most common way to characterize the energy flexibility is by considering it as a static function at every time instant. The validity of this approach is questionable because energy-based systems are never at steady-state. Therefore, in this paper, a novel methodology to characterize the energy flexibility as a dynamic function is proposed, which is titled as the Flexibility Function. The Flexibility Function brings new possibilities for enabling the grid operators or other operators to control the demand through the use of penalty signals (e.g., price, CO2, etc.). For instance, CO2-based controllers can be used to accelerate the transition to a fossil-free society. Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility. In order to validate the proposed methodologies, a case study is presented, demonstrating how different Flexibility Functions enable the utilization of the flexibility in different types of buildings, which are integrated with renewable energies

Numerical and experimental evaluation of ventilation in laboratories: a case study

Ventilation is a key performance requirement in laboratory design as it has to guarantee a safe and comfortable indoor environment. Current standards and guidelines on laboratory ventilation often impose high ventilation rates, increasing the energy need for ventilation, the environmental impact and the energy costs, at many large research facilities. This research focuses on the intra-zonal airflow in a standard laboratory set-up. The airflow and ventilation efficiency is computed with Computational Fluid Dynamics (CFD) and an extensive in-situ experimental case, in which different ventilation strategies are evaluated, has been conducted. The results indicate that the current design standards, which impose a minimal number of air changes per hour, cannot guarantee an optimal, energy efficient design. An optimal design starts from a comprehensive risk analysis. The CFD- simulations and experimental study show that an optimal design should not only be based on a minimal ventilation rate but also has to include an analysis of the impact of the location and type of the ventilation inlet and outlet, the room geometry and ideally the influence of occupants and laboratory appliances. Therefore, it can be concluded that a reformulation of the requirements for laboratory ventilation is appropriate, which in practice will lead to an increasing complexity of the ventilation design process. Although they require a good comprehension and implementation of correct physical properties, CFD-simulations are expected to become an interesting and even mandatory design tool for future, energy efficient laboratory ventilation.status: publishe

Bottom-up modeling of the Belgian residential building stock: influence of model complexity

Demand-side management using the thermal storage capacity of buildings is often suggested as an efficient and economically feasible technology to enable a wide-spread integration of intermittent renewable energy sources. Nevertheless, to quantify the potential benefits of activating the structural storage capacity on a national level, a dynamic bottom-up building stock model is needed. Thereby the aim is not only on the calculation of the annual heat demand, but mostly on an accurate dynamic simulation of the instantaneous heat demand and the indoor temperature, since these are directly linked to active demand response measures. In this paper the suitability of reduced-order models for the application in a dynamic bottom-up building stock model for Belgium is evaluated. Thereby a detailed building energy simulation is compared to reduced-order models of increasing complexity. For the latter both a theoretical approach and a parameter estimation method are analyzed. The building stock description is based on the typical housing approach of the TABULA-project. The reduced-order models show an acceptable prediction of the dynamic temperature profile and heat demand during the heating season, whilst reducing the calculation time significantly. Nevertheless, the reduced-order models are, due to the strong simplifications, less accurate when applied on boundary conditions which significantly differ from the identification data. Especially the coupling between two adjacent rooms is found to reduce the identifiability of the model parameters, resulting in unreliable estimates of inter-zonal heat flows.status: publishe

Robustness of reduced-order models for prediction and simulation of the thermal behavior of dwellings

The integration of buildings in a Smart Grid environment, enabling demand-side management and thermal storage, requires robust reduced-order building models that (i) allow simulation of the energy demand of buildings at a grid-level and (ii) contribute to the development of demand-side management control strategies. System identification is carried out to identify suitable reduced-order models that are able to predict and simulate the thermal response of a residential building. Both grey-box models, based on physical knowledge, and statistical black-box models are considered and identified on data obtained from simulations with a detailed physical model, deployed in the Integrated District Energy Assessment Simulation (IDEAS) package in Modelica. The robustness of identified black-box and grey-box models for day-ahead predictions and simulations of the thermal response of a dwelling is analysed. Whereas accurate day-ahead predictions are obtained for both grey-box and black-box models, the simulated indoor temperatures for the grey-box models tend to gradually deviate from the validation data. Thereby the influence of the data period used for the identification process is found to be of significant importance.status: publishe

Using thermal mass to counter decreasing overall heating system efficiencies in low-energy dwellings

status: publishe

Bottom-up modelling of the Belgian residential building stock: impact of building stock descriptions

Building stock modelling is a key element for the analysis of energy policy scenarios at an aggregate level, such as the integration of buildings in smart grids. To analyse the impact of new technologies and evaluate the dynamic behaviour at an aggregate level, bottom-up dynamic models are a prerequisite. Nevertheless, data on the building stock characteristics is scarce and assumptions need to be made. A comparison of two residential building stock typologies for Belgium is performed in this work with the aim of identifying their differences and investigating how variations in the representation of a building stock can influence the outcome of the model. For this purpose detailed models of the two typologies are implemented and simulated in Modelica using the IDEAS library. Qualitative and quantitative analysis of the heat demand and dynamic behaviour of the stock implementations showed that the inherent differences in the descriptions lead to strong differences in the results, especially when conclusions must be made for specific building cases. This study highlights the need for more reliable and comprehensive data for the building stock, which is a prerequisite for qualitative bottom-up modelling.status: publishe