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: 17th May 2022, 07:10:04 CEST

 
 
Session Overview
Session
Session T3.3: Buildings paving the way for the energy transition
Time:
Thursday, 02/Sept/2021:
13:30 - 15:00

Session Chair: James O'Donnell, UCD
Session Chair: Roel Loonen, Eindhoven University of Technology
Location: Cityhall (Belfry) - Room 3

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Presentations
13:30 - 13:48

A critical review and cost-benefit analysis of green roofs in the United States: an optimization-based energy performance simulation study

Deva Shree Saini1,2, Tarek Rakha1

1School of Architecture, Georgia Institute of Technology, Atlanta, GA, USA; 2Hastings Architecture, Nashville, TN, USA

Aim and Approach

(max 200 words)

Green roofs are often identified as energy-efficient techniques which, through their various mechanisms, contribute to a comfortable indoor environment. A significant number of published literature has investigated the thermal performance of a green roof under various climatic conditions and building parameters. Some of these studies monitored reduction in the thermal flux and roof surface temperature, thereby presenting it as an effective solution for energy savings, but this reduction might not translate into an equivalent drop in energy consumption. Other reviews that focused on the direct energy impacts of a green roof may be perceived as misleading in terms of its energy improvement potential due to their lack of representation of actual thermal parameters. The objective of this paper is to assess the energy performance claims made by previous publications to cast a light on possible exaggerations in green roof energy savings, by using a simulation-based model as the support that considers realistic settings. This was done by modeling a green roof assembly on reference office buildings developed by the US Department of Energy (DOE) for four climate zones. Simulations were carried out in EnergyPlus, and they responded to the variations in climate and the green roof parameters.

Scientific Innovation and Relevance

(max 200 words)

This study’s innovation is the focus on demonstrating the energy savings potential of green roofs as compared to the traditional insulation of a building. The paper poses that green roofs are not effective measures for reducing energy consumption if the base roof’s R-value is equivalent to that of the entire green roof assembly. Therefore, their deployment should not be based solely on their energy savings features but should include other benefits, such as improved air quality, carbon sequestration potential, storm-water management and, reduction of urban heat islands. Contemporary codes and standards for building insulation mandate compliance to recommended values that are typically high enough to have thermal efficiency approximately equal to or greater than provided by a green roof. Considering their high installation, operation, and maintenance costs, the average payback period with such a small amount of savings is considerably high. This paper will also demonstrate a Life Cycle Cost Analysis (LCCA) of a green roof, and convert benefits other than energy savings into a monetary unit to better reflect their economic potential. The paper will discuss an outlook beyond the cost comparison as well, as green roofs can outperform conventional roofs from an ecological and social point of view.

Preliminary Results and Conclusions

(max 200 words)

Multiple scenarios of green roof assemblies were developed based on their identified parameters using a brute-force approach. The annual energy savings for the best combination of parameters for four climate zones were as follows: Arid (Phoenix)-2.8%, Mediterranean (Los Angeles)-2.25%, Tropical (Atlanta)-1.36%, Temperate(Chicago)-1.5%. The simulation study shows limited energy-saving results. The less pronounced results can be attributed to high R-Value of the insulation which prevents the upward heat flow by reducing the thermal gradient created by a vegetated roof. Using the set of design parameters obtained from the preliminary study, optimization will be carried out using GenOpt, a generic optimization program coupled with EnergyPlus, to obtain a set of parameters which will minimize the energy consumption. The outcomes are majorly expected to indicate low insulation values for high energy savings. This study, therefore, aims to bring to notice the significance of a key parameter, insulation thickness, which critically impacts a green roof’s performance by identifying literature that may have inadvertently inflated energy-saving outputs by failing to mention the thermal properties of base roof insulation and the other related factors. This research will present competitively beneficial aspects of green roofs as key factors in establishing it as a sustainable measure.

Main References

(max 200 words)

Castleton, H. F., Stovin, V., Beck, S.B., & Davison, J. B. (2010). Green roofs; building energy savings and the potential for retrofit. Energy & Buildings, 42(10), 1582-1591.

Gagliano, A., Detommaso, M., Nocera, F., & Evola, G. (2015). A multi-criteria methodology for comparing the energy and environmental behavior of cool, green and traditional roofs. Building and Environment, 90, 71-81.

Jaffal, I., Ouldboukhitine, S.E., & Belarbi, R. (2012). A comprehensive study of the impact of green roofs on building energy performance. Renewable Energy, 43, 157-164.

Kotsiris, G., Androutsopoulos, A., Polychroni, E., & Nektarios, P. A. (2012). Dynamic U-value estimation and energy simulation for green roofs. Energy and buildings, 45, 240-249.

Niachou, A., Papakonstantinou, K., Santamouris, M., Tsangrassoulis, A., & Mihalakakou, G. (2001). Analysis of the green roof thermal properties and investigation of its energy performance. Energy and buildings, 33(7), 719-729.

Olivieri, F., Di Perna, C., D’Orazio, M., Olivieri, L., & Neila, J. (2013). Experimental measurements and numerical model for the summer performance assessment of extensive green roofs in a Mediterranean coastal climate. Energy and Buildings, 63, 1-14.



13:48 - 14:06

LSTM hourly solar irradiance prediction using local measurements and weather forecasts

ByungKi Jeon, EuiJong Kim

Inha Univ., Korea, Republic of (South Korea)

Aim and Approach

(max 200 words)

Predicting solar irradiance is important for various predictive control strategies in buildings. we proposed a model to predict the local irradiance using limited input data. The model is data-driven one using Long short-term memory(LSTM). To supply with abundant datasets, the weather data from other unrelated climates or regions are used.

Scientific Innovation and Relevance

(max 200 words)

The proposed model has the following three advantages over the existing solar prediction model. First, the proposed LSTM model trains the model using only the data provided by the weather forecast system, which can be easily obtained with the API. Second, long-term measured data in the target area are not required. As shown in previous studies, most studies require long-term measured weather information in an area to predict local irradiance. Third, the proposed deep learning model does not require additional learning to update the model once it is built. Since the existing deep learning irradiance prediction model uses data measured in a specific region, it is common to update the model by learning the measurement data periodically to improve the performance of the model.

Preliminary Results and Conclusions

(max 200 words)

The proposed model was verified by experiments carried out by local measurement of solar irradiances. The proposed model showed an error of learning performance RMSE 17.4W/m², prediction performance 18W/m² compared to existing weather data testsets. The measurement comparison also showed similar results with the simulation cases.

Main References

(max 200 words)

According to the study of Qing, it was noted that these solar irradiance prediction models are difficult to use for the purpose of controlling residential buildings or small and medium-sized buildings. The reason is that the solar irradiance models can be effectively used only by large-scale operators because past solar irradiance and meteorological data, input data of the learning model, require expensive equipment to measure for a long time in one region and are continuously collected. This means that the existing solar irradiance prediction models need to be improved to apply the aforementioned prediction control algorithm such as MPC. Therefore, Qing's research developed a solar irradiance model that predicts solar irradiance by improving the simple solar irradiance prediction model using only the weather forecast system that is easily available through the Long short term memory (LSTM) deep learning algorithm, which is advantageous for predicting time-series data. This work is similar to this reference, but less measurement and weather information are required for both learning and predicting with slightly higher accuracy.

Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461-468.



14:06 - 14:24

A critical review of the effectiveness of the Sustainability Tracking, Assessment & Rating System (STARS) framework on campus sustainability

Lauren E. Doocy, Arash Zarmehr, Joseph T. Kider Jr

University of Central Florida, United States of America

Aim and Approach

(max 200 words)

Ratings, certifications and assessments provide sustainability goals for design, construction, and operation for the built environment. Standards such as LEED, Energy Star, BREEM, and WELL offer benchmarks for buildings. Recently there has been a push to expand these certifications to small communities and university campuses representing buildings as a part of a larger interconnected system. Modern standards such as the Leadership in Energy and Environmental Design (LEED) for Cities and Communities, the Sustainability Tracking, Assessment, and Rating System (STARS), and WELL community allow individual parts of the built environment to function as a whole. These programs are voluntary, self-reporting frameworks to help small communities and universities track and measure their sustainability progress across measures of energy, water, transportation, and comfort. However, if a built space does not efficiently serve its occupants, the sustainability discussion becomes irrelevant asserting that these standards need to be both prescriptive and performative to increase the health and wellness of occupants in addition to sustainability goals. This study evaluates LEED for communities, STARS, and WELL community’s ability to rate sustainability with the occupants in mind and analyzes several university campuses’ attempts to meet these certifications. We then propose more occupant-centric sustainability measures to enhance human impact.

Scientific Innovation and Relevance

(max 200 words)

This paper compares three rating systems (LEED community, STARS, and WELL community) across several universities. These campuses are a small network of buildings, all fulfilling different functions, and intended to allow students to live, work, and study efficiently. University campuses present an interesting case study because they resemble small communities and act as a living laboratory for small scale simulation of large scale urban spaces. Their resemblance, size, and impact on society, campuses have the opportunity to create change locally and provide insights for larger urban spaces. Each standard hcas different goals. For example, STARS evaluates universities on their overall sustainability including academics, engagement, operation, and planning, the efficiency of the built environment of the campus will have the greatest impact on overall campus sustainability. Similar to LEED certification criticism, STARS has its own shortcomings. While building sustainability initiatives can have extremely positive environmental impacts, impractical buildings are useless and expensive efforts if left unused by the occupants. This paper takes a critical look at the standard and deep look at the energy and water usage on a case-study campus determining guidance to policymakers for code implementation and enforcement.

Preliminary Results and Conclusions

(max 200 words)

Our preliminary results first compare and contrast the three sustainability certifications for small communities and discuss their impact on university campuses and occupant health and wellbeing. For example, STARS puts emphasis on the academic and environmental engagement of students. However, much of the campus environmental impact is overshadowed by the achievement of less pressing initiatives, such as the presence of environment focused courses and on-campus environmental engagement. Campus operational capacity should suggest campus buildings working at maximum efficiency. Further, depopulation of the campus should suggest campus buildings will see a decreased workload. Since the standard university has periodic depopulation of campus, campus buildings should respond accordingly. Without considering building and operation efficiency, STARS cannot accurately evaluate university campus sustainability. We currently have analyzed 100 universities with the stars rating system and are expanding to look at the LEED and WELL standards. Finally, we have taken a deep dive into a case-study university’s individual and campus building, energy, and water data to provide insights and suggestions to help provide a better implementation of these standards across campus.

Main References

(max 200 words)

Habib M Alshuwaikhat and Ismaila Abubakar. 2008. An integrated approach to achieving campus sustainability: assessment of the current campus environmental management practices. Journal of Cleaner Production 16, 16 (2008), 1777-1785.

Peggy F. Bartlett and Geoffrey W Chase. 2004. Sustainability on campus: Stories and strategies for change. MIT Press.

Rebeka Lukman, Abhishek Tiwary, and Adisa Azapagic. 2009. Towards greening a university campus: The case of University of Maribor, Slovenia. Resources Conservation and Recycling 53, 11 (2009), 639-644.

Guy R Newsham, Sandra Mancini, and Benjamin J Birt. 2009. Do LEED-certified buildings save energy? Yes, but… Energy and Buildings 41, 8 (2009), 987-905.

Monika Urbanski and Walter Leal Filho. 2015. Measuring sustainability at universities by means of the Sustainability Tracking, Assessment and Ratings System (STARS): early findings from STARS data. Environment, Development and Sustainability 17, 2 (2015), 209-220



14:24 - 14:42

Building services engineering concept to create plus energy industrial hall buildings

Tobias Blanke1, Bernd Prof. Dr. Döring2, Joachim Göttsche1, Markus Hagenkamp1, Vitali Reger3, Markus Prof. Dr. Kuhnhenne3

1FH Aachen, Solar Institut Jülich, Germany; 2FH Aachen, Germany; 3RWTH Aachen, Chair for Sustainable Metal Building Envelopes, Germany

Aim and Approach

(max 200 words)

New buildings should be nearly zero energy buildings according to the EPBD since 2020. In future new buildings, it will be necessary to optimize the system technology and the use of renewable energies in the design process. In addition, an integrated approach, evaluation and optimization regarding energy demand and energy supply will be necessary. So-called plus-energy buildings even generate more energy than they need for their own requirements on an annual average. This poses special challenges to hall buildings with their high room volumes.

The paper presents a building services engineering concept (BSEC) for industrial buildings to bring them up to plus-energy standards. The BSEC is explained using a sample building, its function is demonstrated by numerical simulations. The concept is based on two key elements: a high-quality building envelope to ensure low transmission heat losses and a heat pump [1], which is connected to the ground via deep-foundation steel piles [2]. The heat pump is supported by solar-thermally activated steel curtain walls, which both supply heat to the ground and serve for cooling at night [2]. The heat is supplied to or removed from the hall via surface heating and cooling elements in the trapezoidal profile of the ceiling.

Scientific Innovation and Relevance

(max 200 words)

The combination of innovative steel products and different buildings services will be shown. The BESC will be explained and the calculation results presented. Besides dimensioning aids for the BESC will be given.

Preliminary Results and Conclusions

(max 200 words)

Plus energy standard for industrial hall buildings are possible. The explained system shows a good combination of the different building service components.

Main References

(max 200 words)

[1] Plusenergiegebäude 2.0 in Stahlleichtbauweise; Vitali Reger Dipl.‐Ing., Univ.‐Prof. Dr.‐Ing. Markus Kuhnhenne, Prof. Dr.‐Ing. Helmut Hachul, Prof. Dr.‐Ing. Bernd Döring, Dr.‐Ing. Thiemo Ebbert, Tobias Blanke, Dr. rer. nat. Joachim Göttsche; 2019; https://doi.org/10.1002/stab.201900034

[2] Nutzung erneuerbarer Energien durch thermische Aktivierung von Komponenten aus Stahl Vitali Reger Dipl.‐Ing., Univ.‐Prof. Dr.‐Ing. Markus Kuhnhenne, Prof. Dr.‐Ing. Helmut Hachul, Prof. Dr.‐Ing. Bernd Döring, Dr.‐Ing. Thiemo Ebbert, Tobias Blanke; 2020; https://doi.org/10.1002/stab.202000031



14:42 - 15:00

Robust optimal identification and scheduling of modernization measures for typical buildings

Jan Richarz, Yuhan Hu, Marco Wirtz, Tanja Osterhage, Dirk Müller

RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate

Aim and Approach

(max 200 words)

Existing buildings play a key role in achieving the worldwide emission reduction targets. In this regard realized modernization measures have long-term effects on the energetic performance of buildings.

Optimization approaches for the modernization of buildings mostly consider one-time investments for the design decision. As boundary conditions, especially technical and economic parameters, change in the future, also the optimal building energy system (BES) configuration will change over the life time of a building. Hence, taking into account multiple points in time for the design decision will result in a BES design that will lead to lower costs and emissions over the life time of a building compared to only considering an initial design.

Richarz et al. [1] therefore developed a mixed-integer linear program (MILP) that generates modernization schedules over the next 30-50 years. Therefore, boundary conditions from prognoses (e.g. energy prices) are set explicitly for each prospective year of the considered time horizon.

This work contains the handling of the prognoses’ uncertainty. The aim is to determine the optimal set of measures under many possible prognoses instead of under one. For this purpose, robust optimization is a promising approach and we hereby present a robust extension of the mentioned MILP.

Scientific Innovation and Relevance

(max 200 words)

Robust optimization is rarely used in the field of energy system planning. Recently, Moret el al. [2] and Hollermann [3] showed promising approaches for its application in energy models.

By calculating modernization schedules, the original MILP ([1]) deals with a mutable building energy system over a time horizon and uses many prognoses. Therefore, taking into account the robustness against significant boundary conditions seems promising to generate more realistic modernization schedules. All investment decisions of the considered time horizon are formulated as the optimization problem. Investments contain modernization measures for the envelope and the energy supply system. Finally, the program returns a modernization schedule i.e. investment path.

Three steps are part of our proposed method. First, based on the proposed approach by [4], all model parameters are categorized concerning uncertainty behavior, and their uncertainty ranges are determined. Second, a sensitivity analysis based on [5] leads to the most influencing parameters of the model. Finally, we use these most influencing parameters for the so-called robust counterparts, built up on the approach by [6]. The extended robust MILP executes a multi-criteria optimization by minimizing equivalent annual costs and emissions. In this study, it is applied on a typical office building.

Preliminary Results and Conclusions

(max 200 words)

In the nominal (non-robust) as well as in the robust solutions, the constellation of the building energy system of the typical building underlies multiple changes over its life cycle. Therefore, we observe advantages in scheduling modernization measures over a buildings’ lifetime compared to one-time investment approaches.

The conducted sensitivity analysis of the original MILP shows that parameters of user behavior, emission factors of energy sources, discount rate, energy prices, and investments in some plants are highly influencing the results.

The robust Pareto frontiers turn out to save emissions less cost-efficiently in comparison to the non-robust ones, which is due to the nature of robust optimization. Furthermore, their shape is different compared to the nominal ones. However, first results show acceptable deviations of the objective values and optimal modernization schedules compared to the nominal solutions. Despite to the robust formulation, still a significant share of carbon emissions can be avoided cost-efficiently.

Depending on the chosen degree of robustness, especially economic and ecological optimum differ over the robust solutions. Concerning the chosen BES in the modernization schedules, we see larger capacities and more gas-based technologies in robust solutions than in less robust solutions.

Main References

(max 200 words)

[1] Richarz J., Eller P., Henn S., Osterhage T., Müller D., Multi-objective optimization for the systematic identification and scheduling of modernization measures for non-residential buildings, ECOS 2020: Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization and Environmental Impact of Energy Systems (ECOS), Osaka, Japan, 2020

[2] Moret S., Babonneau F., Bierlaire M., Francois M., Decision support for strategic energy planning: a robust optimization framework, European Journal of Operational Research, pp. 539-54, 2020.

[3] Hollermann D., Reliable and Robust Optimal Design of Sustainable Energy Systems [dissertation], Wissenschaftsverlag Mainz GmbH, Aachen, 2020.

[4] Moret S., Codina Gironès V., Bierlaire M., Maréchal F., Characterization of input uncertainties in strategic energy planning models, Applied Energy, p. 597–17, 2017.

[5] Heiselberg P., Brohus H., Hesselholt A., Rasmussen H., Seinre E., Thomas S., Application of Sensitivity Analysis in Design of Sustainable Buildings, SDBE 2007: International Conference on Sustainable Development in Building and Environment, Chongqing, China, 2007.

[6] Bertsimas D., Sim M., The Price of Robustness, Operations Research, pp. 35-53, 2004.



 
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