Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 6th July 2022, 15:11:12 CEST

 
 
Session Overview
Session
Session T4.2: Ensuring high quality building simulations
Time:
Thursday, 02/Sept/2021:
15:00 - 16:30

Session Chair: Alessandro Dama, Politecnico di Milano
Location: Cityhall (Belfry) - Room 2

External Resource: Click here to join the livestream. Only registered participants have received the access code for the livestream.
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Presentations
15:00 - 15:18

Embedded single-board controller for Double Skin Facade : a co-simulation virtual test bed

Giovanni Gennaro1,2, Francesco Goia3, Giuseppe De Michele2, Marco Perino1, Fabio Favoino1

1Politecnico di Torino, Italy; 2Eurac Research, Italy; 3Norwegian University of Science and Technology, NTNU, Norway

Aim and Approach

(max 200 words)

Dynamic transparent facades are multi-functional building systems able to change their thermophysical properties (e.g. g-value) in response to external stimuli or control logics, in order to meet different requirements such as energy efficiency and occupant comfort (thermal, visual, IAQ, acoustic). During building operation, in order to optimize the façade configurations according to the various and sometimes conflicting requirements, the design and implementation of the control method and system becomes important to ensure the achievement of the desired performance. The aim of this paper is to describe a framework for the implementation of real-time embedded controller for transparent dynamic facades, applied to a simple case study of a small office environment equipped with a Double Skin Façade (DSF). The core of the controller is the Raspberry Pi 4, a low-cost platform with powerful processors and thanks to its small size it can be easily integrated inside the façade together with the embedded sensors. Several execution and computational tasks can be conducted on this system efficiently, such as data analysis, building energy balances estimation and façade actuators control. Based on the experience and best practices found in literature both a calibrated white-box model developed in EnergyPlus and a rule-based controller have implemented.

Scientific Innovation and Relevance

(max 200 words)

Different control strategies could be implemented for DSF: from simpler rule-based one, to more complex model-based control strategies. The latter could be based on either reduced or physical models, and for both models, depending on the computational time compared to the control timestamp, either an embedded controller (integrated with the façade) or an external processor (exchanging sensed data and actuated variables with a local controller) could be adopted. The present work investigates the potential of embedding model-based and rule-based control strategy in an integrated controller within the façade, relying on low-cost IoT sensors and processors. For this sake, a real case study (i.e. DSF mock-up mounted on an outdoor facility of the Polytechnic of Turin with an operable venetian blind) is used to compare the performance of the two control strategies adopting the following steps: (i) the white model of the DSF (EnergyPlus based) is created and calibrated to experimental data; (ii) the model simulation is synchronized with real-time climate data and embedded in the controller; (iii) for each control time step the white model is adopted to perform control decision making to optimize a certain performance objective; (iv) this process is compared to rule-based decision based on best practices.

Preliminary Results and Conclusions

(max 200 words)

The development of a framework to test embedded controller for dynamic transparent facades presents different challenges and requirements: (i) the update of weather data acquired from a local climate station at each control time step, in order to perform model simulations with the real boundary conditions; (ii) the real-time integration of the measured data with the model; (iii) the computational time for the co-simulation of the models must be less than the time step of the control; (iv) the accuracy of simulated results. The main feature of this framework is the flexibility in terms of control strategies, limited only by the computational performance of the controller, which allows to implement control strategies based on physical, reduced models and simple rules. The computational performance of the Raspberry Pi allows to run several simulations (such as the number of the configuration that the dynamic facades can assume) within each control time step, to predict the optimal DSF configuration, providing very accurate results. Moreover, the integration in a single control system of sensors, control algorithms and actuators could be a robust solution for an embedded controller a dynamic façade, providing a more accurate prediction and decision-making support to the higher-level building control system.

Main References

(max 200 words)

1. Loonen R.C.G.M., Favoino F., Hensen J.L.M., Overend M., Review of current status, requirements and opportunities for building performance simulation of adaptive facades. Journal of Building Performance Simulation 10 (2017).

2. Catto Lucchino E., Goia F., Lobaccaro G., Chaudhary G., Modelling of double skin facades in whole-building energy simulation tools: A review of current practices and possibilities for future developments. Building Simulation 12 (2019).

3. Aftab M., Chen C., Chau C.K., Rahwan T., Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system. Energy and Buildings 154 (2017).



15:18 - 15:36

Pyrano – A Python package for LiDAR-based solar irradiance simulations

Ádám Bognár, Roel C.G.M. Loonen, Jan L.M. Hensen

Eindhoven University of Technology, The Netherlands

Aim and Approach

(max 200 words)

Solar irradiance is a key input for modeling photovoltaic (PV) system performance and the influence of solar heat gains on a building’s energy balance. In the BPS domain, efficient techniques for including the effects of obstructions and reflections have been developed by daylight modeling researchers, but these are often only used to calculate daylight metrics in interior spaces. However, with some adjustments, such methods could also be applied on external built surfaces to calculate the solar irradiance input for PV or solar heat gain simulations.

The goal of this paper is to introduce Pyrano, a new Python package for simulating solar irradiance on external built surfaces. Pyrano consists of five modules:

- A geometry pre-processor that handles irradiance sensor-point placement over EnergyPlus surfaces.

- A LiDAR point-cloud pre-processor for applying geometrical transformations on the LiDAR point cloud to align its coordinate system with the EnergyPlus geometry.

- A Python wrapper to execute certain Radiance sub-programs, such as epw2wea, gendaymtx and dctimestep.

- An input-output module for results visualization and connecting inputs and outputs between solar irradiance and PV simulation software.

- A module for calculating flux-transfer coefficients from pre-processed LiDAR point clouds for efficient matrix-based (sub)hourly annual solar irradiance simulations.

Scientific Innovation and Relevance

(max 200 words)

If a building is situated in the built environment, its solar access is often influenced by reflections or shading by vegetation or other buildings. To be able to take this into account the geometry and reflectance properties of the surroundings need to be known, however, this input is often hard to acquire. The software presented in this work utilizes the 2.5 phase solar irradiance modeling method which was developed with a focus on the requirements of PV system and building solar heat gain simulations in urban context. It allows for taking into account shading and reflections based on the raw LiDAR point-cloud of the surroundings without the need for generating 3D surfaces nor conducting ray-tracing.

Simulation workflows for modeling PV together with a building situated in an urban environment are fragmented. Modeling of urban PV systems requires multidisciplinary knowledge from the modelers about simulating solar irradiance, building physics and electrical systems. Building energy modelers are usually architects, building engineers, or engineers specialized in BPS, rarely electrical engineers. This might have slowed the adoption of including PV for BPS investigations. In an attempt to address this issue, Pyrano bridges the gap between EnergyPlus (building energy), Radiance (irradiance) PVMismatch (PV power) simulations.

Preliminary Results and Conclusions

(max 200 words)

Pyrano was developed in a way that the inputs it uses are compatible with the inputs used by the state of the art ray-tracing software Radiance. This makes it easy to validate the results of the software. The simulations with the proposed method were compared to Radiance simulations with various realistic case studies, showing less than 4% deviation in the simulated annual solar irradiance.

The full-paper will explain the underlying modeling methods and will demonstrate Pyrano with a case study of calculating solar heat gains and PV yield of a building situated in a dense urban environment.

Pyrano is free and open-source. The source code and tutorials are available at: https://gitlab.tue.nl/bp-tue/pyrano. The python package can be installed from: https://pypi.org/project/pyrano/.

Main References

(max 200 words)

Subramaniam, S., 2017. Daylighting Simulations with Radiance using Matrix-based Methods.

Ward Larson, G., Shaskespeare, R., 2003. Rendering with Radiance. Morgan Kaufmann Publishers.

Reinhart, C.F., 2001. Daylight Availability and Manual Lighting Control in Office Buildings – Simulation Studies and Analysis of Measurements.

Mardaljevic, J., 2000. Daylight Simulation: Validation, Sky Models and Daylight Coefficient. De Montfort University Leicester.

Tregenza, P.R., Waters, I.M., 1983. Daylight coefficients. Light. Res. Technol. 15, 65–71.



15:36 - 15:54

A practical approach for modelling PV off-grid systems in EnergyPlus using post-processing of data to identify black out days

Valentina Tomat, Alfonso P. Ramallo-González, Antonio F. Skarmeta-Gómez

University of Murcia, Spain

Aim and Approach

(max 200 words)

Photovoltaic (PV) installations are considered a key element in the fight against energy waste, allowing users to self-produce the energy needed according to their specific demand. Their market spread increased in the last years, because of both the technical advances and more affordable costs. [1]

In remote areas, where the connection to the grid is not possible and it would bring a high cost to bring the power supply, the off-grid PV system is an excellent solution. [2][3] Nevertheless, the sizing of a standalone system in the literature is mainly defined through intuitive methods, numerical methods and analytical methods [4] while, when it comes to model a dynamic simulation, most software does not provide specific tools. Common energy simulation software like EnergyPlus and TRNSYS allow to design grid-tied systems and hybrid systems (grid-tied systems with a battery back-up to avoid outages), but there is almost no literature in how to model off-grid systems. This project aims to propose an equivalent method to simulate the off-grid photovoltaic system with the most popular software: EnergyPlus.

Scientific Innovation and Relevance

(max 200 words)

The main idea behind this paper is to use the simulator EnergyPlus to understand whether or not it is possible to model a standalone system starting from the design of a hybrid system, considering that the two systems are quite similar in design and components. The installation itself hardly provides enough energy to cover the demand throughout the year, because of weather unpredictability and because of unusual peak-demands. In a hybrid system, this scenario is solved by the eventual connection to the grid, that assures energy providing no matter the situation. In off-grid systems, when the installation does not cover the energy demand, black-out days occur.

Considered the similarity of the two situations, simulating a hybrid system, we can expect two main cases: In the first one, the building results to be autonomy all over the year, making the grid connection superfluous (when importation from facility does not occur at all, the building is considered able to operate also in off-grid conditions without outages [5]); in the second case, the model presents electricity coming from the utility: this energy can be converted in black-out days of an equivalent off-grid PV system.

Preliminary Results and Conclusions

(max 200 words)

To the best of the authors’ knowledge, the latter case has never been presented in a scientific paper. To test this method, the case study of an isolated house in the Region of Murcia, Spain, is presented. The building is modelled through SketchUp and Openstudio, while the dynamic energy simulation is obtained through the widely used software EnergyPlus. Results are given considering the number of days in which the battery charge at the end of the day is lower than 10%, i.e. the probability of a blackout day is high. Ten different scenarios are presented, differing in terms of electricity demand profile, PV peak power and battery storage capacity. Afterwards, the same scenarios are simulated with PVGIS, a tool implemented by the European Commission [6] to validate the results obtained with EnergyPlus. PVGIS is set to present the same electricity schedule and the same installation that has been used in EnergyPlus, to allow a more precise comparison. The comparison between the two methods shows good accuracy, inasmuch as the percentage of days varies between 0.5% and 11.9%, i.e., approximating by excess, between 2 and 44 blackout days of difference in the prevision.

Main References

(max 200 words)

[1] Barbose, G. L., Darghouth, N. R., Millstein, D., Spears, M., Wiser, R. H., Buckley, M. & Grue, N. (2015). Tracking the sun VIII: the installed price of residential and non-residential photovoltaic systems in the United States.

[2] Zahedi, A., 2006. Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems, Renewable Energy, 31 (5) (2006) 711-718.

[3] Bekele, G., Tadesse, G., 2012. Feasibility study of small Hydro/PV/Wind hybrid system for off-grid rural electrification in Ethiopia, Applied Energy, 97 (2012) 5-15.

[4] Khatib, T., Mohamed, A., Sopian, K., 2013. A review of photovoltaic systems size optimization techniques, Renewable and Sustainable Energy Reviews 22 (2013) 454-465.

[5] Brumana, G., Franchini, G., Perdichizzi, A., 2017. Design and Performance Prediction of an Energy+ Building in Dubai, ScienceDirect Energy Procedia 126 (2017) 155-162.

[6] Huld, T., Müller, R., Gambardella, A., 2012. A new solar radiation database for estimating PV performance in Europe and Africa, Solar Energy 86 (6) (2012) 1803-1815.



15:54 - 16:12

Initial validation of the one-diode photovoltaic model for the flexible panels

Dominika Knera, Dariusz Heim, Michał Krempski-Smejda

Lodz University of Technology, Poland

Aim and Approach

(max 200 words)

The flexible photovoltaic panels (FPV) became more and more popular in the building applications. In comparison with traditional, mainly crystalline silicon, the thin-film panels characterise by lightweight, low production cost or suitability for curved surfaces. The most popular thin-film PV are the Cadmium telluride (CdTe)/Cadmium sulphide (CdS) [1], as well as amorphous silicon and CIS/CIGS technologies [2]. In presented analysis flexible CIGS and semi-flexible crystalline silicon photovoltaic panels were tested experimentally in two configurations: free-standing (FSPV) and integrated with the wall (WIPV). Two aims of the study were formulated: to compare the performance of both PV panels and to verify the existing one-diode equivalent models implemented in ESP-r.

The experiments were conducted during selected days. Both PV configurations were tested parallel by measurements of power flow as well as the temperature of the modules. Additionally, the basic weather data were also registered, e.g. total and diffuse solar radiation, air temperature. The models of both installations (FSPV & WIPV) was developed in ESP-r using two one-diode photovoltaic models: Kelly’s and Watsun-PV model. The simulation results were compared with experiments.

Scientific Innovation and Relevance

(max 200 words)

Flexible PV technology has the potential to grow with a wide spectrum of application [3]. The advantage of them in comparison to stiff panels is visible in case of uneven surfaces. Additionally, building integrated flexible PV (BIPV) panels are usually much lighter than traditional. Those features make flexible PV more and more popular in building construction sector.

The existing PV models in ESP-r were validated for traditional crystalline PV modules equipped with glass sheets and aluminium frame [4]. The original part of this study is model validation for flexible thin-film PV under normal and extreme temperature. For FSPV the panel temperature slightly rises above the ambient air temperature because the effect of air cooling is high. When the PV panel is tightly joined with the building wall (WIPV) the risk of overheating rises rapidly and the effect of temperature on PV efficiency is much more visible. Additionally, the spectral characteristics of incident solar radiation will be analysed considering the visual and infrared part monitored on-site.

Preliminary Results and Conclusions

(max 200 words)

The models of both constructions (FSPV & WIPV) and two photovoltaic technologies (c-Si and CIGS) were defined in ESP-r. The simulations were conducted using two one-diode photovoltaic models: Kelly’s and Watsun-PV model. The initial results show the temperature and power flow for analyzed PV modules. The difference between power production and temperature is recognized. The experiments were done for clear and cloudy skies conditions. The final conclusion will be formulated based on the comparison between experimental and numerical results.

Main References

(max 200 words)

[1] Visa I, Burduhos B, Neagoe M, Moldovan M, Duta A. Comparative analysis of the infield response of five types of photovoltaic modules. Renew Energy 2016;95:178–90. https://doi.org/https://doi.org/10.1016/j.renene.2016.04.003.

[2] Pandey AK, Tyagi V V., Selvaraj JA, Rahim NA, Tyagi SK. Recent advances in solar photovoltaic systems for emerging trends and advanced applications. Renew Sustain Energy Rev 2016;53:859–84. https://doi.org/10.1016/j.rser.2015.09.043.

[3] Ramanujam J, Bishop DM, Todorov TK, Gunawan O, Rath J, Nekovei R, et al. Flexible CIGS, CdTe and a-Si:H based thin film solar cells: A review. Prog Mater Sci 2019:100619. https://doi.org/10.1016/j.pmatsci.2019.100619.

[4] Mottillo M, Beausoleil-Morrison I, Couture L, Poissant Y. A comparison and validation of two photovoltaic models. Can. Sol. Build. Conf., 2006.



 
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