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 Dec 2021, 21:48:00 CET

 
 
Session Overview
Session
Session T2.8 (Online Track): Buildings paving the way for the energy transition
Time:
Thursday, 02/Sept/2021:
10:30 - 12:00

Session Chair: Vincenzo Corrado, Politecnico di Torino
Location: Virtual Meeting Room 2

External Resource: Click here to join the Zoom Meeting
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Presentations
10:30 - 10:48

Further development and validation of the "PROFet" energy demand load profiles estimator

Kamilla Heimar Andersen, Synne Krekling Lien, Karen Byskov Lindberg, Harald Taxt Walnum, Igor Sartori

SINTEF AS, Norway

Aim and Approach

(max 200 words)

Recent developments in policy highlight the importance of utilizing end-user flexibility to support the decarbonization of the energy system [1], [2]. Energy flexibility of buildings is also a research topic within the Norwegian research center for Zero Emission Neighbourhoods (FME-ZEN), where a ZEN definition is being developed considering several key performance indicators (KPI) [3]. The energy flexibility KPIs evaluate how well a building/a neighborhood can respond to signals from the grid and manage its demand, storage, and local generation to optimize its response to such signals while satisfying user comfort [4], [5]. To establish such KPIs, standard load profiles (i.e., unaware signal profiles) should be used as a reference to evaluate against on the flexibility potential.

Previous work [6] has developed a model, here named PROFet, for forecasting aggregated weather dependent load profiles of buildings, based on energy measurements from buildings connected to district heating, collected in a database here named trEASURE. The database and the model have hourly resolution and treat energy use for heating and electric specific purposes separately.

The aim of this study is to present further developments of the trEASURE database and to validate the typical load profiles from the PROFet model with out-of-sample datasets.

Scientific Innovation and Relevance

(max 200 words)

First, the measurements database trEASURE has been extended to include more than 300 entries representing ca. 2.4 million m2 of floor area, subdivided into 11 building categories, both residential and (mostly) non-residential buildings. Second, the heating measurements have been pre-treated with a decomposition of the domestic hot water (DHW) part, using a hybrid seasonal/energy signature method [7]. Third, the energy efficiency of the buildings has been inferred by comparing the temperature dependency of the space heating measurement data with reference values from building standards. This resulted into three levels of energy efficiency: efficient - representing low-energy and passive house buildings; intermediate - representing newer buildings built according to the 2010 code, TEK10, which is also regarded as an ambitious yet realistic target for energy-efficient renovations [8]; and regular - for all other buildings.

Besides, the typical load profiles from the PROFet model had not yet been validated with out-of-sample datasets; this is presented in this paper.

Preliminary Results and Conclusions

(max 200 words)

New sets of coefficients have been generated by the PROFet model with respect to the initial work [6], thanks to the extension and the improvements in the underlying trEASURE database. The resulting load profiles for given locations and years are compared with out-of-sample datasets, i.e., energy measurements that are not included in the trEASURE database. The out-of-sample datasets are limited to few building categories, such as apartment block, office, and school, and to the regular energy efficiency standard. This is due to difficulties in creating large-good quality datasets, with respect to data availability (incl. of metadata) and the need for time-consuming data cleansing routines. It should be noted that since the PROFet model is meant to generate load profiles valid at an aggregated level, also the out-of-sample datasets need to be conspicuous to have a meaningful comparison. The validation is based on both graphical comparisons for typical days and on statistical indicators, following the ASHRAE Guideline 14 [9].

The resulting load profiles for a standardized Oslo climate will also be presented graphically and as tabulated values, and it will be discussed how these profiles can be used to develop the energy flexibility KPIs in the Norwegian ZEN definition.

Main References

(max 200 words)

[1] European Commission, “Clean Energy for All Europeans - The Winter Package.,” 2018.

[2] IEA, “Status of Power System Transformation 2019, Technology report — May 2019,” 2019.

[3] M. K. Wiik, S. M. Fufa, I. Andresen, H. Brattebo, and A. Gustavsen, “A Norwegian zero-emission neighborhood (ZEN) definition and a ZEN key performance indicator (KPI) tool,” IOP Conf. Ser. Earth Environ. Sci., vol. 352, no. 1, 2019.

[4] R. G. Junker et al. (2018) Characterizing the energy flexibility of buildings and districts, Applied Energy, Vol. 225, pp. 175-182.

[5] IEA EBC (2019) Characterization of Energy Flexibility in Buildings, deliverable of Annex 67 Energy Flexible Buildings.

[6] K. B. Lindberg, S. J. Bakker, and I. Sartori, “Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts,” Util. Policy, vol. 58, no. March, pp. 63–88, 2019.

[7] S. K. Lien, D. Ivanko, and I. Sartori, “Domestic hot water decomposition from measured total heat in Norwegian buildings (Paper submitted for review),” in Build-Sim Nordic 2020.

[8] Enova (2012) Potensial- og barrierestudie – Energieffektivisering I norske bygg, Enova report 2012:01 (in Norwegian).

[9] ASHRAE (2014) Measurement of Energy, Demand, and Water Savings, ASHRAE Guideline 14-2014.

30159_Andersen_Kamilla Heimar.pdf


10:48 - 11:06

The efforts towards development of an energy efficiency upgrade platform

Mahnameh Taheri, Loic Jacob, Colin Parry, Sahar Mirzaie, Agnieszka Hermanowicz, Ioanna Vrachimi, Alan Wegienka

arbnco Ltd., United Kingdom

Aim and Approach

(max 200 words)

The building sector can have a significant contribution in emission reduction by energy retrofit actions. Thus, policymakers are making building energy performance a main concern in their sustainability decision makings. However, the building industry usually relies on simplified assessment tools and methods, which does not address optimized energy and cost saving strategies at a large scale. This is due to, among other reasons, the required expertise, time, and effort as well as complexity of the analysis [‎1]. This contribution presents the efforts towards development of an energy assessment platform to assist policy makers, building engineers, energy managers, etc., to evaluate cost effective retrofit actions and provide the best return from investments for commercial buildings. Portfolio assessments commonly fall into three phases of benchmarking, detailed investigation, and scaling of findings across portfolio [‎2]. The methodology involves, clustering the buildings based on their energy performance, selecting the most influential group for further analysis, and performing detailed investigations for the selected group. This study covers development of i) a GUI for convenient easy collection of building data, ii) a recommendation engine connected to a database of retrofit and renewable options with associated energy and cost savings, and iii) an investment decision engine.

Scientific Innovation and Relevance

(max 200 words)

Rapid and reliable energy performance assessments have been one of the main concerns of the building science community. Improving the building energy performance predictions and addressing the shortcomings of the conventional approaches encouraged the development of new techniques. As mentioned above, the presented platform enables the users to a) benchmark their buildings’ energy consumption, b) audit their building energy features, and c) explore different energy retrofit, renewable, and investment options. At the screening level, the analysis is based on limited user input, including, minimum twelve months of metered energy consumption data together with building location, area, and year of build. The core of the analysis behind the platform is sophisticated load shape analysis and Machine Learning (ML) methods. Load shape analysis is used here for the purpose of benchmarking based on a set of predefined efficiency factors [3]. Moreover, a novel application of a disaggregation algorithm for commercial whole building energy data is used to disaggregate the consumption data into categories of weather dependent, scheduled, and base loads. Load shape analysis, benchmarking building energy efficiency, and energy disaggregation are the backbone of the development of the enhanced investment recommendation engine.

Preliminary Results and Conclusions

(max 200 words)

The project presented here provides a portfolio-scale energy assessment decision-making service. The result is a set of energy efficient alternatives for building elements and systems which improves the energy performance, considering the user-specified constraints. The process starts with an initial portfolio screening, to identify worst performing buildings with the greatest energy saving potential for further detailed analysis. For this step, a GUI is developed for a convenient and easy collection of more detailed user-dependent data. An inexperienced user can provide the very basic building data using the interface. Some unspecified building input parameters are then estimated based on typical characteristics of a building, such as type, age, location. The platform suggests actions, including energy efficiency retrofits, related to building fabric, lighting, and HVAC efficiency, renewables, i.e., PV and wind, and storage options. Then the financial feasibility of different investment alternatives is scoped. The financial metrics for these assessments include net present value, internal rate of return, simple payback, discounted payback period, etc. The data-based approach presented in this paper is used to evaluate different components of a buildings' energy profile and opens up the possibility of identifying the biggest retrofit opportunities with minimal specialist time over large building stocks.

Main References

(max 200 words)

1. Lee, S.H., Hong, T., Sawaya, G., Chen, Y., Piette, M.A. 2015. DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings. ASHRAE Winter Conference, January 2015, Chicago.

2. Franconi E. M. and Bendewald M. J. 2014. Analyzing Energy-Efficiency Opportunities across Building Portfolios. American Council for an Energy-Efficient Economy, ACEEE, Summer Study on Energy Efficiency in Buildings.

3. Taheri, M., Rastogi, P., Parry. C., Wegienka, A. 2019. Benchmarking Building Energy Consumption Using Efficiency Factors. Proceedings of the 16th International IBPSA Conference, Building Simulation 2019. September 2019, Rome, Italy. DOI: 10.26868/25222708.2019.210575.

30910_Taheri_Mahnameh.pdf
30910_Taheri_Mahnameh_c.pdf


11:06 - 11:24

Alternative modelling approaches to energy performance certificates

David Jenkins, Peter McCallum, Sandhya Patidar, Sally Semple

Heriot-Watt University, United Kingdom

Aim and Approach

(max 200 words)

The outputs required from building simulation are impacted by the requirements of building energy policy, and the direction of that policy towards low-carbon energy systems. To model total energy consumption of a building is a different task to characterising the demand of that building at a transient level; to do so at scale is an additional level of complexity. With the success and ubiquity of Energy Performance Certificates (EPC) across Europe, there is tendency to use this form of building assessment as the main vehicle for characterising building energy demand. However, there is growing evidence of EPCs being applied to areas for which they were not designed to serve. By comparing a range of alternative building energy modelling techniques with the current methodologies underlying EPCs, this study proposes future directions for standardised energy assessment of residential buildings and the important role of modelling choices within this. Alternatives to traditional steady-state models for standardised assessment are proposed, with a framework for critiquing new methods proposed. The work will be placed within the wider framework of the Energy Performance in Buildings Directive (EPBD), discussing the extent that alternative methods may fit within this (noting existing responses to the EPBD across Europe).

Scientific Innovation and Relevance

(max 200 words)

There is an information gap between the modelling used in research and that applied for standardised energy assessments of buildings. This paper aims to overcome the assumption that state-of-the-art building simulation and/or statistical modelling is inherently too complex and inefficient for use with EPC-type assessments. The work particularly focusses on two areas of modelling: i) Theoretical dynamic simulation that is applied at scale within an efficient interface and ii) statistical modelling of empirical data, by way of a clustering analysis to allow for classification of different homes/households by their measured energy demand characteristics. The dynamic simulation is based on currently available simulation software but applied through bespoke techniques that efficiently gather data on buildings from GIS/OS, open-source energy data, and existing EPC assessment data. The statistical model approach applies models developed by the authors that apply k-means clustering to proxy parameters of energy use via Principal Component Analysis, alongside disaggregation techniques to better categorise aspects of transient demand. Placing such techniques within a framework that is commensurate with EPC-type assessments raises the prospect of bridging the gap between large-scale generation of energy ratings of buildings (used in policy to encourage market transformation) and more detailed building simulation.

Preliminary Results and Conclusions

(max 200 words)

The work proposes a dynamic simulation approach and statistical empirical method that could be used as a starting point for a new form of standardised energy assessment. However, in doing so, the paper also proposes standard criteria that any new method of energy assessment (for generating some form of energy rating) could be critiqued against. These criteria reflect the need for future energy assessments to align with some definition of reality, better understand the importance of (transient) demand flexibility, accommodate new technologies, be suitable for punitive action on homeowners (noting the direction of building energy policy across Europe), are suitable for extrapolation and standardisation, and are able to set a standard for quality of input information. By discussing the proposed modelling methods against these metrics of success, the paper will demonstrate what is specifically possible with the novel models generated by the authors, but also set a framework under which future energy assessments could be judged against. Initial results indicate that the traditional assumption that simple steady-state models are the only option for large-scale energy assessment of residential buildings is no longer valid, and the modelling community should work to update this approach to EPCs.

Main References

(max 200 words)

Beckel, C., Sadamori, L., Staake, T., & Santini, S. (2014). Revealing household characteristics from smart meter data. Energy, 78, 397–410. https://doi.org/10.1016/j.energy.2014.10.025

Hardy, A., & Glew, D. (2019). An analysis of errors in the Energy Performance certificate database. Energy Policy, 129, 1168–1178. https://doi.org/10.1016/j.enpol.2019.03.022

Lomas, K. J., Beizaee, A., Allinson, D., Haines, V. J., Beckhelling, J., Loveday, D. L., Porritt, S. M., Mallaband, B., & Morton, A. (2019). A domestic operational rating for UK homes: Concept, formulation and application. Energy and Buildings, 201, 90–117. https://doi.org/10.1016/j.enbuild.2019.07.021

McCallum, P., Jenkins, D. P., Peacock, A. D., Patidar, S., Andoni, M., Flynn, D., & Robu, V. (2019). A multi-sectoral approach to modelling community energy demand of the built environment. Energy Policy, 132, 865–875. https://doi.org/10.1016/j.enpol.2019.06.041

Semple, S., & Jenkins, D. (2020). Variation of energy performance certificate assessments in the European Union. Energy Policy, 137, 111127. https://doi.org/10.1016/j.enpol.2019.111127

30173_Jenkins_David.pdf


11:24 - 11:42

Predicting the effects of building energy conservation policies: Modeling decision-making of building owners and tenants to maximize their own profits

Atsushi Funabiki1, Yuki Oto2, Shohei Miyata1, Yasunori Akashi1

1Department of Architecture, Graduate School of Engineering, The University of Tokyo, Japan; 2Obayashi Corporation

Aim and Approach

(max 200 words)

At the 21st Conference of the Parties (COP21) to the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, the Paris Agreement was adopted as an international agreement on environmental issue [1]. In response to this agreement, Japan has also set a goal to reduce carbon dioxide emissions. To achieve this goal, the spread of environmentally friendly buildings is required urgently, and support from a political perspective is essential to make buildings become more energy efficient.

To make policy which maximizes energy conservation efficiently, it is necessary to predict the effects of the policies. However, the interactions of building owners and tenants on building performance have not been considered in this context. Therefore, in this study, we built a simulation model for an office building area which consists of building owners and tenants as agents and predicts agents’ decision makings based on their performances. The purpose of this study is to contribute to the planning of the optimal policies by predicting the effects of energy conservation policies including their combination. In this paper, Multi-Agent Simulation (MAS) which is one of the bottom-up simulation was employed.

Scientific Innovation and Relevance

(max 200 words)

In addition to, Europe and the United States, Japan is also promoting the introduction of energy-saving policies, with subsidies, tax incentives, loans and other forms of assistance. In Japan, there are subsidy programs for buildings announced in Building Energy Efficiency Act of the Ministry of Land, Infrastructure and Transport and several other ministries [2-4]. In the real world, it is difficult to evaluate energy conservation policies in buildings by means of demonstrations, because of the huge economic and time costs involved, and it is generally verified by using simulations. Previous studies have modeled decision-making with residents and building owners as agents, aiming to predict greenhouse gas emissions from new construction and renovation, and facility upgrade, and to manage facility space efficiently [5-8]. By contrast, there are few simulations that incorporate multiple stakeholder decision-making, including subsidies for tenants, and few that examine the synergistic effects and counteracting effects of combinations of policies, therefore, it is difficult to consider in advance whether current policies are effective. It is novel and meaningful to incorporate multiple agents and policies into the simulation model constructed in this study to enable the prediction of energy performance and evaluation of energy-saving policies for buildings.

Preliminary Results and Conclusions

(max 200 words)

In this paper, the objective policies are The Building Energy Efficiency Act, new construction subsidies and renovation subsidies. The behavioral logic of the agents in the simulation is as follows. The building owners change rents, rebuild or renovate to maximize theire profits. Tenants, on the other hand, move to new offices when they meet their preferences. At this time, the tenant chose a building that maximizes its own utility based on the rent and building performance of the building. The main four results of previous simulations are as follows. 1: Large subsidies are needed to make a significant impact on new construction subsidies. 2: Renovation subsidies can have an early effect, but the effects may converge in the long run. 3: the appropriate percentage of subsidies may vary depending on the combination of policies. 4: To progress the energy conservation, it is necessary to raise tenants' awareness of energy conservation, and although it will take time, it will have a significant effect. Further studies are needed to investigate policies to help tenants give more weight to energy efficiency in their preferences for building choices.

Main References

(max 200 words)

[1] UNCFCC, The Paris Agreement.

https://unfccc.int/files/essential_background/convention/application/pdf/english_paris_agreement.pdf.

[2] Ministry of Land, Infrastructure and Transport, Building Energy Efficiency Act.

https://www.mlit.go.jp/common/001134876.pdf.

[3] Ministry of Economy, Trade and Industry, Agency for Natural Resources and Energy,

https://www.meti.go.jp/main/yosan/yosan_fy2019/pr/en/shoshin_taka_12.pdf.

[4] Ministry of the Environment, http://www.env.go.jp/earth/earth/ondanka/mat31y_01-03.pdf.

[5] Nägeli, C., Jakob, M., Catenazzi, G., Ostermeyer, Y., 2020. Towards agent-based building stock modeling: Bottom- up modeling of long-term stock dynamics affecting the energy and climate impact of building stocks, Energy and Buildings, 211, 109763.

[6] Liang, X., Yu, T., Hong, J., Shen, G.Q., 2019. Making incentive policies more effective: An agent-based model for energy efficiency retrofit in China. Energy Policy, 126, 177-189.

[7] Jia, M., Srinivasan, R.S., Ries, R., Weyer, N., Bharathy, G., 2019. A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings, Energy and Buildings, 199, 352-367.

[8] Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., Djurovic-Petrovic M., 2010. A review of bottom-up building stock models for energy consumption in the residential sector, Building and Environment, 45, 1683-1697.

30733_Funabiki_Atsushi.pdf


11:42 - 12:00

Urban climate simulation: coupling of mesoscale meteorological model with building-resolved neighborhood CFD simulation

Jan Carmeliet1, Aytaç Kubilay1, Dominik Strebel1, Dominique Derome2

1ETH Zurich, Switzerland; 2Université de Sherbrooke, Canada

Aim and Approach

(max 200 words)

Cities show a heat island effect, where night temperatures are markedly higher in urban areas than rural ones. Cities are thus particularly affected when struck by heatwaves, which are, amongst others, climate extremes occurring at increasing frequency under the course of climate change. It is imperative to understand by how much the urban climate during heat waves could be mitigated using different solutions. Such solutions can be permanently in place, like evaporative cooling pavement materials or trees, or seasonal, like awnings and sun shading devices, to in-sync with heat waves, such as watering and irrigation interventions.

However, the simulation of the local urban climate at the scale of a few buildings to a full neighbourhood must consider the following physical processes, local wind dynamics, urban ventilation, solar radiation entrapment and local shading, evapo-transpiration of vegetation, heat and mass transport in porous media lining the streets, wind-driven rain deposition and run-off, and local anthropogenic heat sources.

To do so, we model local urban climate by coupling mesoscale meteorological simulation results to building-resolved computational fluid dynamics, coupled to radiation, heat-air-moisture transport model and building energy simulation. This paper shows the potential of this approach from mesoscale to

building- and material-scale.

Scientific Innovation and Relevance

(max 200 words)

xMeteorological models provide boundary conditions that are realistic, as they use local topography and integrate land-use models. However, their local precision is limited by many factors: coarse resolution, parametrization of different phenomena and computational limitations. Coupling of meteorological models, to provide the boundary conditions of CFD models, which resolve a full neighborhood spatially, is an on-going research field, where multiple approaches exist with different advantages and computational cost. At neighborhood scale, our CFD model is coupled to a radiation exchange model and a heat and mass transport model, allowing to take into account all the required physics. The development of methodologies incorporating all relevant urban elements under realistic climate loading should be accompanied by interface with a comfort and physiological models of occupants, as well as methods for health impact assessment. These assessments have to be done under different scenarios of future climate. The validation of such coupling is complex and could use, for example, large datasets of careful urban measurement campaigns. In this paper, we present a validation exercise for the components of the modeling approach and present avenues for full coupling validation.

The approach presented provides capacities to assess the potential of urban mitigation solutions for heat waves.

Preliminary Results and Conclusions

(max 200 words)

We present the sub-models and the couplings strategies of the modeling framework and provide one example of its application for an urban square in Zurich in terms of the impacts of using alternate solutions of pavements and adding vegetation.

Sustainable mitigation solutions to local urban heat islands, especially in case of heat waves, are expected to require a combination of measures such as the use of ventilation lanes, shadowing, evaporative cooling, vegetation, reflective surfaces. Realistic climate boundary conditions are necessary to deal with such extreme cases and analyses at local scale are required in order to assess the local heat islands and assess correctly the impact of these mitigation measures. The proposed coupled simulation approach is a carefully developed framework, providing a physically sound and validated representation of the complex local urban climate.

Main References

(max 200 words)

Ferrari A, Kubilay A, Derome D, Carmeliet J. 2020. The Use of Permeable and Reflective Pavements as a Potential Strategy for Urban Heat Island Mitigation. Urban Climate 31: 100534.

Kubilay A, Derome D, Carmeliet J. 2018. Coupling of Physical Phenomena in Urban Microclimate: A Model Integrating Air Flow, Wind-Driven Rain, Radiation and Transport in Building Materials. Urban Climate 24: 398–418.

Kubilay A, Derome D, Carmeliet J. 2019a. Impact of Evaporative Cooling Due to Wetting of Urban Materials on Local Thermal Comfort in a Street Canyon. Sustainable Cities and Society 49: 101574.

31125_Carmeliet_Jan.pdf