A DECISION SUPPORT METHOD FOR PERFORMANCE-BASED RESIDENTIAL BUILDING DESIGN AND RETROFITTING

Technical field of the invention

This invention is a web-based decision support method for the development and adoption of energy-efficient residential buildings by providing a positive feedback cycle that addresses the low awareness of energy efficiency in the housing sector.

State of the Art

The building sector, which is at the centre of important problems such as unsustainable built environments, climate change and energy insecurity, has a serious potential to also solve such problems. However, there is currently an issue on the efficiency gap is brought up, which defines the difference between the level of investment actually made in energy efficiency and a higher level that is technically and economically feasible. Even though the barriers that cause efficiency gap differ from country to country and city to city in terms of importance, it is known that the most basic barrier is knowledge gap.

Awareness raising and information activities, which are of key importance in reducing this knowledge gap, are handled within the scope of many policies, and efforts are made to establish a qualified supply-demand balance in the building sector. This effort is crucial to avoid urban areas where inefficient building stocks with low adaptability are accumulated, which may result in high costs in the coming years, given the long lifespans and high energy consumption levels of buildings. Especially for Turkey, where there is a discordance between energy supply and energy demand, and rapid urbanisation and high population growth is experienced, the current limited knowledge and expertise, and lack of awareness on the supply (architect, engineer, contractor, investor and other stakeholders) and demand (building owner, tenant) sides deepens this efficiency gap. In order to eliminate the negative impact of this situation on the country's energy bill, there is an intense agenda to ensure full compliance with legal regulations and, moreover, to produce sustainable settlements. In this context, concentrating on residential buildings, which have the highest share of energy consumption in the building sector and further have the highest energy saving and improvement potential, offers great opportunities, especially in this process where urban transformation works that foresee the change of a large part of the existing building stock accelerate.

However, an important obstacle to realising these opportunities is the lack of awareness, by the decision makers in the supply and demand sides of the currently fragmented housing sector, of cost-effective energy-saving applications and technologies or of their possible positive impacts on residential energy, economic and environmental performance. Performance-based housing production, which is seen as the primary solution to such an obstacle, necessitates radical changes in the housing production market based on conservative approaches in which minimum requirements specified in the legislation are only provided focusing on low initial investment costs, and from a predominantly weak innovation cycle. To achieve these changes, it is necessary to reduce the barrier between housing design or residential retrofitting and performance and to provide the best guidance to decision makers in the early design process, which has the highest impact on housing performance.

In the state of the art, the scope of the current energy efficiency legislation is insufficient. The TS 825 Standard of Thermal Insulation Requirements for Buildings and related application regulations mainly consider heating energy conservation. Information on energy consumption for cooling, lighting, ventilation, hot water purposes and the use of renewable energy sources, which are ignored, should be included in the energy identity document that the Building Energy Performance Regulation requires to be issued. However, it is seen that this regulation is insufficient in practice in that building owners, building users and other decision makers are not sufficiently informed about the benefits or effects of the energy identity document which is seen as an effective tool for creating a demand-driven market for energy-efficient high- performance buildings. In addition, this regulation does not provide the necessary guidance on determining the energy performance level in a way that will ensure the cost effectiveness level in the economic life of the building.

According to the relevant decision-making circles, the level of compliance in Turkey with the countrywide energy legislation is estimated to be around 25-30%. On the other hand, it is estimated that the legal regulations are fully implemented in only 10-15% of new buildings. This insufficient level of compliance with energy legislation is attributed to the lack of appropriate control mechanisms and the low level of awareness of consumers (i.e., building owners).

Because the traditional perception of how a residential building should be produced is still valid in the construction industry, decision makers are left alone with regard to how to implement energy legislation and regulations regarding sustainable housing production. Therefore, architects, who are at the centre of the design process, adopt approaches based on widely accepted traditional rules of thumb, architectural examples, past project experience and intuition within the scope of “tried and tested methods”. Thus, it can be said that the decisions that guide the early design are made with limited knowledge, or their possible effects on the building’s energy performance are not sufficiently considered. In the last stage of a housing project developed, an expert verified the compliance of the project with the legislation using the semi-dynamic BEP-TR software program (the building energy performance calculation method used in Turkey; Republic of Turkey Ministry of Environment, Urbanisation and Climate Change), which is based on the national calculation method. Therefore, the differentiation of models considered in the domains of design and performance calculation eliminates the option of direct feedback and paves the way for all kinds of errors and misunderstandings in the early design phase. On the other hand, no studies have been conducted to determine the effects of building retrofitting changes on the building’s energy performance.

To minimise the efficiency gap, it is vital for decision makers to consciously make energy- and cost-efficient choices. However, there is no decision support platform where decision makers can be aware of cost-effective applications and technologies that will save energy and of their positive effects. Even if there is a dispersed information infrastructure, the efficiency gap cannot be sufficiently reduced if decision makers have insufficient knowledge and poor understanding of energy consumption and life cycle costs, are unfamiliar with recommended practices and technologies for improving building performance, have insufficient confidence in the accuracy of such information and have weak control over construction companies, which consider only the minimum initial investment cost and minimum standards in the production of residential buildings. The invention, which is the subject of the application numbered “AU2020102451 ” in the state of the art, covers a wide variety of aspects, elements and protocols, consisting of a series of interconnected sub-sections to construct a sustainable building.

In the state of the art, systems for constructing a sustainable building are mentioned. However, there are no systems that include reducing the barrier between housing design or residential retrofitting and performance and providing the best guidance to decision makers in the early design process, which has the highest impact on residential performance. Unless the gap between the need for information and access to information on current housing production and residential retrofitting processes is eliminated, it does not seem possible to establish a qualified supply-demand balance in the housing sector. This situation raises the need to provide an adequate and constructive information flow to consumers (building owners et al.) and manufacturers (architects et al.) who play active roles in the early design phase but have limited knowledge. Thus, a system is needed that reduces the discordance between the knowledge needed and access to such knowledge in the transition from the conservative approach to the performance-based approach to housing design and residential retrofitting.

Due to the problems described above and the inadequacy of the existing solutions on them, it was necessary to come up with a development in the relevant technical field.

The aim of the invention

This invention is a decision support method for performance-based residential building design or retrofitting.

The most important aim of the invention is to provide a decision support platform that enables rapid and iterative feedback to early design phase decisions.

Another important aim of the invention is to solve the problem of low market demand and low awareness level regarding energy efficient residential buildings, thus enabling a qualified supply-demand balance to be established. Another important aim of the invention is to ensure the development of an energy- and cost-oriented decision support tool that will enable informed decisions regarding sustainable, high-performance housing production at the early design stage.

Another important aim of the invention is to provide sustainable housing production through a web-based decision support platform that will facilitate access to multiobjective design solutions with an integrated feedback loop that overlaps with the principle of early design exploration and iteration.

Another important aim of the invention is to provide comparison of different design alternatives to ensure that the decision maker focuses on important design parameters in new housing design or residential retrofitting.

Another important aim of the invention is to provide, in new housing design or residential retrofitting, comprehensive information support to the target user through data on annual primary energy consumption, together with data on annual energy consumption for heating, cooling, lighting and hot water, the ratio of renewable energy usage, annual final energy consumption; life cycle cost (LCC) of the building’s economic performance together with its initial investment cost and annual operating cost; and annual greenhouse gas (GHG) emission value in relation to its environmental performance.

The structural and characteristic features of the invention and all its advantages are explained more in Figure 1 and the detailed description written with reference to this figure. Thus, the evaluation should be based on this figure and detailed explanation.

Description of drawings

Figure 1 is the schematic flow diagram of the method that is the subject of the invention.

Reference numbers

100. Decision support method

101. Defining the design parameters over the web interface 102. Defining the performance indicators over the web interface

103. Defining the design space input parameters over the web interface

104. Retrieving the simulation input files created by the simulation applications from the database

105. Retrieving the design space performance simulations performed by the simulation applications from the database

106. Receiving, processing and calculating the simulation output files by the server

107. Transferring the data calculated by the server to the database

108. Performing Pareto optimisation by the server

109. Presenting solutions to the user via the web interface

Description of the invention

This invention relates to a web-based decision support method for the development and adoption of sustainable, energy-efficient residential buildings by providing a positive feedback cycle to address the low awareness of energy efficiency in the housing sector.

The decision support method (100) provides the decision maker a wide range of information by enabling the transformation of design inquiries into design actions in accordance with both national legislation and passive building standards, considering the materials and construction techniques commonly used in the local market. This method uses a comprehensive design space search approach that is based on the integration of parametric building energy simulation and multiple-criteria decision analysis.

In the process step of defining the design parameters over the web interface (101 ), the decision support method (100) defines building-scale design parameters (plan type, building height, roof type and transparency ratio [total window area I total facade area]) and design parameters at settlement scale (settlement form, ratio of building height to street width [H/W] and orientation) to define the model geometry. In the definition of the building form for the housing model, using modules with a floor area of 100 m ² is projected. In this context, two different plan types were defined: a square floor plan that combines four modules with a form factor (building length I building depth in the plan) of 1 .00, and a rectangular floor plan that combines two modules with a form factor of 2.00. Within the framework of the defined plan types, three different building heights (9 m, 15 m and 30 m) were considered with 3, 5 and 10 floors, respectively, where the height from floor to floor was accepted as 3 m. In terms of the roof type, a pitched roof and a terrace roof were considered. The transparency ratio was assumed to be the same on all facades of the residential building model, with three different values (30%, 40% and 50%).

Since the urban canyon is defined in a two-dimensional section by ignoring street intersections and assuming that buildings are of semi-infinite length along the canyon axis, the evaluation of adjacent (row) blocks was considered appropriate for analysing the effect of the considered H/W ratios on the outdoor conditions, and of the detached blocks were considered appropriate for analysing the effect of the outdoor conditions to which the buildings were exposed from four directions. In this context, on the basis of the data defined on the housing model, a pavilion settlement form was defined with square-based buildings, and pavilion and slab settlement forms were defined with rectangular-based buildings. The settlement forms were based on a 3-by-3 matrix according to a uniform configuration of nine blocks with the same characteristics on a hypothetical site. The block at the centre of the matrix layout is the target building model on which the relevant analyses were performed.

The H/W value was regarded as 1 .00 (uniform canyon) in all the settlement forms, and the optimum orientation angle was 0° for settlements based on residential buildings with a rectangular floor plan and 90° for settlements based on residential buildings with a square floor plan. In addition, proportional street sections were defined by taking the H/W ratio as 1.00 for all floors. In the residential settlement models that were considered to have had 3, 5 and 10 floors, the street widths were 9 m, 15 m and 30 m, respectively. TS12576 and TS 12174 standards were considered in the arrangement of the spaces between the buildings for all the models. In this respect, two-way bicycle paths were added in accordance with the expansion of the street spaces by up to 30 meters in the 10-floor settlement model. In the three-floor settlement model, a one-way vehicle road was defined for the 9m street width, whereas in the 5-floor settlement models, two-way vehicle roads were considered for the 15m streets. In terms of geometry, different residential settlement alternatives were defined depending on the settlement form, H/W ratio and orientation in the settlement scale, and on the plan type, number of floors, roof type and transparency ratio in the building scale.

Layering details of opaque and transparent components in terms of optical and thermophysical properties of the building envelope were determined based on the limit U (W/m ² K) values specified for Istanbul in the Standard for Thermal Insulation Requirements in Buildings-TS 825, as shown in Table 1 .

Table 1 : Characteristics of housing model elements.

The airtightness value of the building envelope was considered ‘high’, and the rate of natural ventilation air changes was assumed as 0.5 h’ ¹ . Within the scope of building energy systems, it was accepted that the heating energy demand was met by a central hot water boiler and that there was a radiator system in the housing modules. The energy type used was natural gas. Split air conditioners were defined for cooling energy. It was assumed that stand-alone electric water heaters were used for the hot water system. The luminousness level for each housing module was defined as 150 lux. The building occupancy schedule was created based on the official research on the Turk family structure within the scope of the use of buildings, and the user density was regarded as 0.04 m ² /person. The user activity level was defined as 1 10 W/person. Regarding the operating hours of energy systems based on the occupancy schedule, it was assumed that the indoor air temperature during the heating period was 20°C for 07:00-23:00 hours and 13°C for other hours, and for the cooling period, 26°C for 07:00-23:00 hours and 32°C for other hours. It was also accepted that natural ventilation was active in the cooling period. In terms of climatic conditions, Istanbul was considered the representative province of Turkey’s temperate-humid climate zone (the second climate zone according to the TS 825 standard, Csa, based on the Kbppen classification).

The revision dates of the TS 825 standard and the building permit statistics for Istanbul were considered in defining suitable model configurations for different construction years of the model building in terms of residential retrofitting. In addition, due to the continued urban transformation works based on the demolition and reconstruction of housing stocks before 2000, building configurations before 2000 were neglected, and three different building classes were defined — the first period, 2000-2008; the second period, 2008-2013; and the third period, post 2013. The limit U values (W/m ² K) of the building envelope components and the efficiency values of the energy systems varied in the creation of the three housing models related to the building classes and are given in Table 2.

Table 2: Characteristics of housing model configurations

In the process step of defining the performance indicators over the web interface (102), the key performance indicators (KPIs) were defined as the primary energy consumption (PEC) and the LCC for new housing design, and the primary energy (PE) savings and the LCC savings for residential retrofitting. In this way, the developed decision support tool facilitates communication between architects and residence owners, who are the primary target users, and enables these two important decision makers to focus on important design parameters in new housing design or residential retrofitting and compare different design alternatives. In addition, to provide comprehensive information support to the target user with this developed tool, data on the annual energy consumption for heating, cooling, lighting, and hot water; ratio of renewable energy usage; annual final energy consumption along with the annual PEC; initial investment cost and annual operating cost (i.e., energy cost + maintenance & repair cost) along with the LCC; and annual GHG emission value were presented for each design alternative in new housing design. As for residential retrofitting, in addition to the PE savings and LCC savings, the GHG savings and discounted payback period (DPP) were determined for each design alternative.

Defining the design space in a way that meets the criteria of current and future building codes and the requirements of the decision makers is important to achieve a high level of effectiveness of the developed decision support tool. In the process step of defining the design space input parameters over the web interface (103), the said parameters were classified into the following four groups: geometry (for the settlement and building scales), building envelope, energy systems and renewable energy systems. On the other hand, to develop a reliable tool, national and international standards and current housing market analysis studies were considered to determine variable parameter ranges and distributions, and parameters with insignificant effects on energy performance were neglected. In this context, when the input parameters of the design space were examined, different design parameters, such as the settlement and building scales, were clearly considered in terms of geometry, and thus, most residential buildings in Istanbul can be represented with the different residential settlement models developed. Along with the definition of the detached and row settlement forms based on the optimum H/W ratio and orientation angles (0° for rectangular plans and 90° for square plans) in the settlement scale, the number of floors (3, 5 or 10), the roof type (terrace or pitched), and the transparency ratio (30%, 40% or 50%) for the square and rectangular plan types played an active role in presenting a wide range of information to be transferred to the decision maker. For the settlement form alternatives developed based on the seven parameters within the scope of geometry, the 13 parameters in Table 3 (third column from the left) were also considered in terms of the building envelope, energy systems and renewable energy systems.

Table 3: Characteristics of design space input parameters

Table 4: Characteristics of the defined glazing types

These 13 parameters are the exterior wall core material, exterior wall thermal insulation type, exterior wall thermal insulation thickness, roof thermal insulation type, roof thermal insulation thickness, ground floor thermal insulation thickness, window glazing type, solar control element type, heating system efficiency value, cooling system efficiency value, heating-cooling system type, hot water system efficiency value and rooftop photovoltaic (PV) system. Thus, many design parameters have been defined in terms of geometry, the building envelope, energy systems and renewable energy systems, encompassing solutions that range from compliance with the requirements of the current national building standard TS 825 to combinations of solutions that enable buildings with nearly zero energy (e.g., passive house U-values and use of PV systems). Moreover, the design space has been diversified with the alternatives developed based on the stated parameters.

In the process step of retrieving the simulation input files created by the simulation tools from the database (104), the input files, called the IDF files, which are needed by the simulation programs in the preprocessing step of the parametric simulation process, were created. Input files describing the settlement form and the building to be analysed for both new housing design and residential retrofitting were created using DesignBuilder, the comprehensive interface of the EnergyPlus simulation program. During the preprocessing, 162 main IDF files were created, which differed in terms of settlement and building geometry as well as in the use of solar control elements, and these IDF files were manipulated according to the different variable ranges given in Tables 3-4 for the building envelope design parameters, resulting in new IDF files that described each design alternative. Full factorial sampling was used for these derived files. This procedure was repeated according to the underlying parameter tree.

In the process step of retrieving the design space performance simulations performed by the simulation tools from the database (105), dynamic energy simulations were performed by manipulating the parameterised design space in the text-based IDF files.

In the process step wherein the server receives, processes and calculates the simulation output files (106), the performance indicators were calculated by processing the output files of the parametric energy simulations that were performed. For the new housing design, the PEC was calculated using the following equation-1 : Equation-1 where E _CO ns,fuei is the annual energy consumption based on the type of fuel (kWh/m ² - year), EPV is the annual amount of energy generated by the PV system (kWh/m ² -year), fp.fuei is the PE conversion coefficient by fuel type and f _p ,pv is the PE conversion coefficient related to the electricity generated by the PV system. In Turkiye, the PE conversion coefficients within the equation based on the type of fuel consumed are 1 .00 for natural gas and 2.36 for electricity. The PE conversion coefficient used for the electricity generated with the PV system was accepted as the same as the PE conversion coefficient of electricity defined for Turkey. The annual degradation in the power output of the PV modules was regarded as 0.5% per year.

For the residential retrofitting, the PE savings was calculated using the following equation-2: 100

Equation-2 where PECait is the annual PEC of the design alternative (kWh/m ² -year), and PECbuiiding_ciass is the annual PEC of the housing model to be retrofitted according to the building class (kWh/m ² -year).

For the new housing design, the LCC was calculated using the following equation-3:

LCC = I + Repl — Res + E + OM&R

Equation-3 where I is the initial investment cost (TL/m ² ), Repl is the present value of the replacement cost (TL/m ² ), Res is the present residual value (TL/m ² ), E is the present value of the energy cost (TL/m ² ) and OM&R is the present value of the non-fuel operating, maintenance and repair cost (TL/m ² ).

For the residential retrofitting, the LCC savings was calculated using the following equation-4: 100

Equation-4 where LCCait refers to the LCC of the design alternative (TL/m ² ), and LCCbuiiding_ciass refers to the LCC of the housing model to be retrofitted according to the building class (TL/m ² ).

The two important components of the LCC calculations are the calculation period and the costs. The calculation period was accepted as 30 years. In the cost calculations, the costs of the building components with no effect on the building energy performance, and the costs that were the same within the context of the alternatives, were not considered. The current market unit costs, based on the price proposals of the suppliers, were used to determine the initial investment costs of the alternatives in the design space. These costs are presented in Table 3 in Turkish liras (TL) and in euros (€).The unit costs included only the material prices. The timing and number of the building system replacements depended on the estimated lifespan of the system and the length of the calculation period. Within this context, the calculation period that was used in this study encompassed the lifespan of the variables related to the building envelope and no replacement was foreseen. The lifespan of the components of the energy systems was obtained from Annex A of the EN 15459 standard, and the annual maintenance and repair costs of these systems were also calculated based on this annex. The maintenance and repair costs related to the PV system components (the PV module + the balance of system) considered within the scope of renewable energy systems were also considered in the calculations. The energy costs were calculated based on the local energy prices together with the energy consumption according to the fuel type and the energy generated from the PV systems. From the life cycle perspective, the residual values were calculated for the components whose lifespan was longer than the specified calculation period. To determine the present values, the considered costs, other than the initial investment costs, were discounted in comparison to the year when the calculation started, that is, 2019, based on a discount rate of 3%. In addition to the LCC calculations, the DPP values within the framework of the same data and assumptions were calculated using the following equation-5:

Equation-5 where ACOP is the operational cost (E + OM&R) savings (TL/m ² ), I is the initial investment cost (TL/m ² ), / is the discount rate and t is the calculation period.

Furthermore, sensitivity analyses were carried out to define the level of uncertainty of the economic data, which can cause significant differences in the results of the LCC calculations, to increase the reliability level of such calculations in the decision-making process. In the sensitivity analyses, the discount rate and the energy price development were considered, as they can significantly impact the evaluation of the economic performance. The assumptions from the base case calculations and from the results of the sensitivity analyses are as follows:

Base case: The discount rate is 3%, and the energy price development is neglected.

Sensitivity analysis I: The discount rate is 6%, and the increase in energy prices is neglected.

Sensitivity analysis II: The discount rate is 3%, and the energy price increase is set at 15% annually.

Sensitivity analysis III: The discount rate is 3%, and the energy price increase is set at 30% annually.

Regarding the environmental performance of the design alternatives, the GHG and GHG savings values were calculated using the following equations 6-7:

Equation-6 where fco2,fuei is the country-specific emission factor per fuel type (kgCO2-eq/kWh) and f CO2,PV is the conversion factor for the avoided CO2-eq emissions based on the electrical energy generated by the PV system (kgCO2-eq/kWh). For Turkey, the respective emission factors for natural gas and electricity were 0.234 and 0.418 kgCO2- eq/kWh, respectively. 100

Equation-7 where GHGait is the annual GHG emissions of the design alternative (kgCO2-eq/m ² - year) and with the GHGbuiiding_ciass is the annual GHG emission of the housing model to be retrofitted according to the building class (kgCO2-eq/m ² -year).

In the process step of transferring the data calculated by the server to the database (107), it is ensured that the calculated data regarding the new housing design and residential retrofitting were kept under record within the parametric analysis.

In the process step of performing Pareto optimisation by the server (108), Pareto solutions (trade-off solutions) that fit the preferences of the decision makers were determined. In this respect, design alternatives with high energy performance are preferred by architects, while design alternatives with low LCCs are priority for residence owners. Therefore, in the performed Pareto optimisation, the objective functions, that is, minimisation of the PEC and the LCC for the new housing design, are given in equation-8; and for the residential retrofitting, the highest realised PE savings and LCC savings are given in the following equation-9:

Min { fi(x),f ₂ (x) } x = [X1, X ₂ , ....Xm]

Equation-8

Max { f ₃ (x),f ₄ (x) } x = [xi, x ₂ , ....x _m ]

Equation-9 where /1 is the PEC (kWh/m ² -year), f ₂ is the LCC (TL/m ² ), fs is the PE savings (%), /4 is the LCC savings (%), x is the combination of design variables and m is the number of design variables. These Pareto optimisations were performed separately for all sensitivity scenarios considered within the scope of the LCC calculations.

The process step of presenting solutions to the user via the web interface (109) is intended to make these datasets, which were transferred to the database and had too many and complex structures to be displayed at once, easy to understand. The same process step also provides decision makers fast feedback, which are needed at the early design stage. An interface is provided through which the relationships between the input parameters and the performance indicators can be easily manipulated (according to the defined parameter ranges), displayed and understood; from which information can be extracted and explored; and on which decision makers can compare design alternatives for solving the problem by considering multiple conflicting objectives at the same time, thereby gaining new knowledge and a new perspective on the selection of energy-efficient design alternatives by better understanding the problem.

The process steps of the decision support method (100), which enables the development and adoption of energy-efficient residential buildings by providing a positive feedback cycle to the low awareness level in the housing sector, are as follows:

• Defining design parameters over the web interface (101 ),

• Defining performance indicators over the web interface (102),

• Defining design space input parameters over the web interface (103),

• Retrieving simulation input files created by simulation applications from the database (104),

• Retrieving design space performance simulations performed by the simulation applications from the database (105),

• Receiving, processing and calculating simulation output files by the server (106),

• Transferring data calculated by the server to the database (107),

• Performing Pareto optimisation by the server (108),

• Presenting solutions to the user via the web interface (109).