Generating proper building envelopes for ...

Accepted Manuscript Title: Generating proper building envelopes for photovoltaics integration with shape grammar theory Authors: Amr M.A. Youssef, Zhiqiang (John) Zhai, Rabee M. Reffat PII: DOI: Reference:

S0378-7788(17)31944-8 https://doi.org/10.1016/j.enbuild.2017.09.077 ENB 7996

To appear in:

ENB

Received date: Revised date: Accepted date:

4-6-2017 17-8-2017 25-9-2017

Please cite this article as: Amr M.A.Youssef, Zhiqiang (John) Zhai, Rabee M.Reffat, Generating proper building envelopes for photovoltaics integration with shape grammar theory, Energy and Buildings https://doi.org/10.1016/j.enbuild.2017.09.077 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Generating Proper Building Envelopes for Photovoltaics Integration with Shape Grammar Theory Amr M. A. Youssef 1,2, Zhiqiang (John) Zhai 1*, Rabee M. Reffat 2 1

Department of Civil, Environmental and Architectural Engineering, University of Colorado, UCB 428, ECOT 441, Boulder, CO 80309, USA. 2 Department of Architectural Engineering, Assiut University, Assiut 71518, Egypt. * Corresponding author:[email protected]

Abstract Building integrated Photovoltaics (BIPV) receives growing attentions from both architectural and energy saving perspectives. Large commercial building envelopes can be utilized due to their great potential of reducing building energy consumption and increasing PV integration impact, especially in climate zones with rich solar resources. Most current studies have been focused on predicting electricity generation of BIPV systems with existing envelope geometries, while few studies have discussed the generation of proper envelope shapes for PV integration due to the challenge of integrating architecture and engineering. This paper introduces a novel optimization method for BIPV shape development based on the shape grammar theory. The method reforms given building shapes/envelopes to produce a set of better BIPV shape alternatives, as well as determines the best placement and matching BIPV systems for the optimized envelopes. The main set of criteria considered during the generation and optimization process include PV power generation, PV economic impact and building energy consumption. Architectural preferences are included in generating preferred design alternatives, such as view consideration and shape direction. Commercial buildings in Egypt are used to demonstrate and validate the applications of the developed method and tool. The method and tool can help designers in achieving an optimal design of building envelope that is most suitable for maximizing PV integration.

Keywords: Building integrated photovoltaics; Shape grammar; Optimization; Building envelope; Power generation; Computational tool.

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List of symbols: Building Integrated PhotoVoltaics System Advisor Model (a software tool) Window-to-Wall-Ratio Genetic Algorithm Generic Optimization Program Shape Grammars Rule number (i) Ratio Variation number (i) (ax, by) denotes to the ratios of V(i) on a 2D perpendicular module. For example, (2x, 3y) refers to the ratio of 2 modules in x direction and 3 modules in y direction, and the unit in the studied treatments equals 1m. PV positions (a), (b), (c) or A specific position in a treatment for PV integration; (a), (b), (c) and (d) are in a descending order based on the PV generation in (d) each treatment Graphical User Interface GUI Modification Level M.L. PV Generation Level PV G.L. Architectural Preferences A.P. BIPV SAM WWR GA GenOpt SG R(i) V(i) V(i) (ax, by)

1. Introduction Building integrated Photovoltaics (BIPV) receive growing attentions in achieving net-zero energy buildings, especially when rich solar resources are available. External façades of high-rise commercial buildings are needed for PV integration in addition to the roof, because the roof-mounted PV can barely generate 40.4 kWh/(ft2.yr) in hot climates while high-rise commercial buildings require 13.4 kWh/(ft2.yr) based on the ASHRAE standard (ASHRAE 90.1-2007) – the roof can supply only 3 floors of electricity. PV integration with external facades is inevitable for tall buildings, and it may also deliver more architectural creativity to building facades. The challenges in BIPV design are the high PV cost and low efficiency, and accordingly building envelope forms should be optimized to best utilize PV modules. Hence, an optimization method is required for reforming a given building envelope towards better ones for optimizing PV generation and overall building energy cost. For instance, extruding a southern surface in a hot climate zone (2A) can provide additional 62% PV generation if the same PV modules were attached on the extrusion roof rather than the surface itself, predicted using SAM (System Advisor Model) (NREL 2014).

Various studies were found focusing on building envelope optimization for energy efficiency. You et al. (2013) proposed an integrated approach to evaluating facade designs from daylighting,

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thermal performance and natural ventilation perspectives. Tuhus-Dubrow and Krarti (2010) developed a method to optimize selected envelope properties of residential buildings. Ouarghi and Krarti (2006) examined commercial building envelope shapes using Generic Algorithms (GA) and Neural Network methods to optimize energy and construction cost. Caldas and Norford (2003) developed an optimization tool to determine building envelope features that minimize HVAC and lighting energy and their costs. Other studies were aiming to optimize PV installation and its integration within high quality architecture. IEA SHC (International Energy Agency – Solar Heating & Cooling Program) - Task 41, carried out during 2009 to 2012, included many other categories of tools and methods for architects (Maria et al, 2012). Hwang et al. (2012) presented an analytical optimization for PV module inclinations and related spacing distances between them in office building facades. Choudhary et al. (2008) proposed a design analysis process for obtaining a functional net-zero energy solar house using PVs.

Many tools/methods were developed to evaluate solar energy impact, in general, suggesting optimal building geometries. For instance, Youssef et al. (2016) introduced an optimization framework using GA via the GenOpt platform, which can determine the best integration of options in building envelope features (building dimension, orientation, etc.) to reduce building net energy consumption and find best PV utilization and PV integration placement. Youssef et al. (2015) proposed a method for optimizing BIPV envelopes; it formulated the best orientation variations (as a variable) on BIPV surfaces to maximize solar exposure using sensitive analyses. Building surfaces can then be varied towards sensitive orientations to generate alternatives with higher solar exposure as a first step towards the optimization. Selecting the most matching PV modules for these surfaces comes as a following step. A computational tool “RADIANCE” can assist in analyzing solar irradiation and optimizing urban geometric forms (Kampf and Robinson 2010). A simulation program “GRIPVS” was developed by Sui and Munemoto (2007) to study the optimal gable roof shape with lower CO2 emission and higher investment value. “SOLVELOPE” program can generate 3D envelope shapes for a given site to meet annual heating needs from solar energy (Topaloğlu 2003; Capeluto et al. 2005). These studies do not provide articulated building envelope shape alternatives to maximize PV performance; this articulation may include the envelope shape, surfaces, better integrated PV systems, possible PV positions and

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others, in addition to a computational method to generate better alternatives accordingly. No approaches or tools were found in literature for automatically formulating BIPV alternatives with best PV utilization.

This paper presents an optimization methodology that can generate better energy-based alternatives of building envelope shape using diverse criteria for a given building design. The method was implemented in computer software and a graphic user interface (GUI) was developed to demonstrate the method and its application for commercial buildings in Egypt. The optimization process is based on required criteria and specified priorities selected by the designer/user. The criteria are to achieve better building energy net consumption, PV economic impact, or a balance between these two. The priorities are relevant to architectural preferences. The optimization of a building envelope can be conducted in two phases as shown in Figure 1: a) optimizing the basic building shape; b) optimizing the building surfaces. The architectural design theory of "Shape Grammars" is used to achieve the optimization as detailed in the following sections.

2. Principles of Shape Grammar Theory Shape grammar (SG) theory, invented by Stiny G (1980), is "a set of shape rules that can be applied in a step-by-step way to generate a set, or language, of designs", according to Terry Knight. SG provides multiple representations for generating shapes' alternatives to achieve a specific purpose, and performs computations for shapes' alternatives by recognition of a particular shape and its possible replacements. These computations are applied using various developed rules that specify the particular shape replacement and how they are replaced based on each case. The rule is represented by two shapes, separated by an arrow (Shape A  Shape B); the pattern on the left hand-side or similar ones can be replaced with the other one on the right-hand side in specific cases. Accordingly, many designs can be generated computationally as shown in Figure 2. Many studies used SG to achieve architectural goals computationally. Ruiz-Montiel et al. (2013) presented a SG based system for generating different designs to satisfy a set of architectural requirements. Granadeiro et al. (2013) introduced a SG based methodology to produce better envelope alternatives with minimum HVAC demand. Halatsch et al.

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(2008) discussed the applicability of a procedural modeling of SG in urban planning to derive meaningful 3D city models.

This paper employs SG to generate envelope shape designs by using primitive shapes and rules of interaction among them, and better shapes and envelope treatments are generated computationally from both PV power generation and building energy use perspectives. The example shown in Figure 2 starts with an initial shape (Figure 2 (a)) and two rules (Figure 2 (b)); each rule is represented by the pattern on the left hand-side or similar ones to be replaced with the other pattern on the right-hand side. However, the specified two rules are just shifting internal shapes, so the position of shapes denoted with a curser is critical, and the direction of the shift in both rules depends on the spatial transformation used for making the match. When a shape is matched to the left side of a rule, the matched shape in the design is then replaced (shifted) as indicated in the relevant rule. Then, applying SG rules may have many choices/ways through phases of derivation (Figure 2 (c)). Accordingly, many designs can be generated computationally in each step as shown in Figure 2 (d).

3. Framework of the Proposed Optimization Method Figure 3 illustrates a summarized framework for the proposed optimization method that consists of five sections as detailed below.

3.1. Main inputs An initial base shape for a given building in a specific land layout is needed to start the optimization. The initial shape could be a rectangle, L-shape, U-shape or any basic shape, and the related floor dimensions, building height, window-to-wall-ratio (WWR) [1], and building orientation can be specified as a range or a specific value; these ranges can be used for: a) generating many starting shapes for following optimizations so as to provide a large number of alternatives; and/or b) selecting

[1]

The position of windows on the given surfaces was not considered in this study, while WWR indicates the areas of solid and glazed areas on surfaces to be integrated with opaque and semi-transparent PVs, respectively.

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the optimal values in terms of building energy consumption using GA – this optional optimization step has been detailed in the previous study (Youssef et al., 2016).

3.2. Simulation The initial base case/cases identifies the specified inputs and their ranges (if applicable) is/are simulated using DOE-2 engine (DOE-2, 2014), in order to determine its/their energy consumption(s) for the following comparisons (this simulated energy consumption includes electricity and gas consumption of studied models, and any other consumers can be set to ASHRAE standards). However, if wide ranges of inputs were specified, the optional optimization using GA can run first.

3.3. Phase 1: optimization of the building geometry Optimizing building geometry (if required) can be performed by applying protrusions and indentations on the given shape. A pool with 72 SG rules has been developed as shown in Figure 4 to yield a vast number of alternatives for the given case; these rules represent different ratios and orientations for different protrusions and indentations. Applying each rule or group of rules will affect building energy performance and other rules to be applied afterwards; therefore each rule has been studied and classified in terms of different criteria (e.g., energy consumption, PV generation, etc.). Preferences (e.g., linear, central, etc.) and effects on each of the other rules; these classifications and relevant examples are shown in Table 1 and Figure 5, respectively. For example, R19 has a low effect on modifying the shape, while R50 has a higher effect due to the protrusion ratios as shown in Figure 5 (a). Also, considering a northern/southern view requires applying a protrusion towards the east or west sides (e.g., R15 and others), while R15 will not help if approaching a western or eastern view is required as shown in Figure 5 (b), and R42 leads to linear shapes due to the ratios of the subtracted indentation. However, the order of applying these rules is sensitive also in reaching alternatives as shown in Figure 5 (c) and (d), for example, applying R50 avoids some other rules (e.g. R3) to be applied afterwards as shown in Figure 5 (e). This serial application may lead to inapplicable shapes or inappropriate shapes which have to be avoided in the application process [2], although each rule can be

[2]

In this study, shapes that have been avoided from the generation process are:

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applicable separately on the initial basic case. The performance of each rule cannot be judged first since different rules’ adjustments can be applied on base cases that can also be different in shapes, dimensions and/or orientations; and this will lead accordingly to a huge number of cases to be generated; this is why each generated case should be simulated individually using DOE-2 engine to be compared with the given base case. The Phase 1 outputs are the optimized building geometries that meet specified criteria, and each alternative will be the input for the following optimization phase.

3.4. Phase 2: optimizing alternatives' surfaces to be integrated with PV: Surfaces of optimized shape alternatives should be reformed and treated to receive better solar exposure and PV performance. Three treatments were studied in the current optimization as examples: a) Perpendicular surface protrusion that represents one of the most popular modifications to building facades with the increased surface area for PV integration; b) triangular protrusion that provides the ability to utilize specific orientation for PV with a medium modification to the facade; and c) shade and/or louver for the lowest modification to building facades with the ability to utilize their surfaces for PV integration. All these treatments have the advantage of increasing shaded areas on the facades as well on different levels, and can be applied to building surfaces with appropriate ratios. The improvements on PV power generation using various ratios and PV positions (surfaces) in different orientations for each of these three treatments have been simulated using SAM (NREL 2014), and this PV generation is also considered in the final building energy balance. The best ratios have been selected as shown in Table 2 and Figure 6, along with the needed horizontal and vertical distances between each other to avoid the self-shading determined using Autodesk ECOTECT (Autodesk, 2014).

a) inapplicable shapes which refer to discontinuous or unclosed shapes; this is why all the developed rules and base cases are (and lead to) continuous and closed shapes. b) inappropriate shapes architecturally which include: - shapes with an outline ratio more than 1:3 (width: length): this is achieved through studying the relationship between rules and their serial applications. - shapes with nonfunctional dimensions for office building wards; 8 m (26.2 ft) has been set as a minimum accepted edge length in a generated shape. - shapes with irrational protrusions or indentations: irrational dimensions refer to very small dimensions compared to the whole width (even if they were longer than 8m (26.2 ft)); this is why the rules are rational to the base case (e.g. 0.25 from the width), not stand-alone dimension. - shapes that have less floor area than the base case: this is to generate shapes with adequate floor area to the architectural needs; hence the base case floor area is the minimum area to be accepted in the generated shapes; rules are applied accordingly.

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Figure 6 presents the best ratios and PV positions for each of the three specified treatments in different orientations with requirements of the developed SG rules used on the building facades. The PV positions in each treatment have been ordered descending (a, b, c and d (if any)), while the positions that generate less than the initial surface of study have been avoided from the order as shown in Figure 6 (c). All the illustrated calculations in Figure 6 were simulated using one of the best PV modules as a reference; this module has the best performance with appropriate price among others (Youssef et al., 2015). Another module of the best ones has been selected to be integrated in windows, since the first module will not be appropriate for that; both modules are detailed in Table 3. Changing the module affects only the calculated values of the introduced treatments, and does not affect the treatments' optimization in PV performance or the order of best PV positions. The Phase 2 optimization provides the better surfaces' alternative/s regarding PV performance as compared to the initial case.

3.5. The outputs After applying Phase 1 and Phase 2 optimizations, the obtained output is optimal alternative(s) with treated surfaces and PV integration compared to the initial case based on the user specified criteria (as detailed below). Specifically, the outputs of this framework include optimized BIPVs alternatives presented with their net energy consumption before and after PV generation, building area, facades that are integrated with PV, PV positions on treated surfaces, and PV performance details (payback period, required PV area and initial cost).

Phase 1 and 2 can be repeated to generate a vast number of optimal BIPV alternatives till specific design criteria are met or a specific number of alternatives are generated (the loop end). The internal process of each phase (mainly the SG selection) can be tracked to achieve specific criteria as detailed next.

4. The Optimization Criteria Phase 1 and Phase 2 in the proposed optimization framework may reach different solutions with different design criteria. The criteria can be classified to: a) essential criteria that are mandatory, such as building energy generation and reduction, land boundaries and specific shape requirements; and b)

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additional criteria that can be specified by users optionally to filter the generated alternatives based on the preferences (if any), such as, the level of modification from the basic shape, and the level of PV integration, and the architectural preferences. Table 4 illustrates essential and additional criteria that are covered by this study, along with their internal classifications and an explanation of how they can be achieved using the specified two phases. 5. The surrounding buildings’ consideration In order to consider the surrounding buildings that cast shadows on the proposed building for optimization, the required distances from different neighbors’ heights in different orientations were predetermined using Autodesk ECOTECT (Autodesk, 2014). There are two approaches to consider the determined distances: a) Approach 1: applying the SG rules that can only provide optimized building shapes that cannot exceed the determined sunny area; this can be processed in Phase 1 of the framework; and b) Approach 2: excluding PV integration from alternatives' facades located in the shaded area, and this can be processed in Phase 2.

6. The tool implementation A computational tool has been established to generate the best BIPV alternatives for different applications based on the proposed framework. The core of the tool has been coded used MATLAB language (Mathworks 2015) with a friendly Graphical User Interface (GUI), which contains the main inputs required for the optimization as detailed before. The main inputs can be classified into: a) mandatory inputs such as the site and building details (dimensions, an initial shape and its relevant details) and the optimization criteria as explained before; and b) optional inputs that include the modification level, the PV Integration level and so forth. All the relations among the inputs have been studied and previewed in the established GUI to be inserted easily and provide appropriate outputs.

Computationally, the reference case will be determined after the simulation with given inputs, or using the GA optimization to determine the optimal input values among the specified ranges. The user will then be asked whether he/she would like to proceed to generate another alternative, and whether he/she would like to generate more treatment variations on the selected alternative. The tool outputs

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include, for each generated alternative, the final building shape, dimensions, area, best placement in the specified site, and the final energy consumption after considering PV performance on its optimized surfaces, the PV payback period and positions. All these will be presented visually for the final alternatives with descriptions as well as a text file for all the alternatives details.

7. Applications of the proposed tool Different cases of commercial buildings in Egypt were used as applications to demonstrate the proposed method and tool, and the Cairo weather file and ASHRAE standard 90.1 – 2007 (ASHRAE 2007) have been used. Figure 7 shows the details of optimizing a simple cubic commercial building to generate 12 alternatives based on the illustrated framework, and different optimization criteria have been used as inputs to demonstrate the framework. The outputs (alternatives) shows different optimization levels due to their relevant specified criteria. For example, the improvement in energy consumption vary from 1.8% to 12.5% after applying PVs in alternative 3 and 9, respectively; this difference is due to the normal modification and low PV generation required in alternative 3, while those criteria in alternative 9 are both high. The architectural preferences have been achieved in relevant cases as shown in Figure 8; for example, considering a southern view in alternatives 1 and 2 has been improved compared to the relevant reference case (the length of facades approaching southern edge has been extended), and alternatives 5 and 6 remain central shapes as required, while alternatives 11 and 12 have been moved towards linier shapes compared to the relevant reference case.

Table 5 shows other cases (case 1 is the case detailed before) that have different integrations of inputs to cover all the inputs' options illustrated before. Some options are already determining/requiring other ones; for example, case 2 specified any shape as an input; hence no modification level can be selected, and these relations are illustrated with relevant colors in Table 5.Figure 8 contains the best solution produced for each of the 12 cases in terms of the net energy consumption, while Figure 9 shows the improvement of solutions in terms of the net energy consumption and architectural preference (if any) compared to their reference cases. The improvement in the 12 cases varies from 3.1% to 20.5% in net energy consumption; it varies among cases due to specific required inputs, for instance, decreasing

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the modification degree will certainly decrease the ability to reach the possible minimum energy consumption and considering the view if required.

8. Validation First, the values of the net energy consumption calculated by the tool for some cases have been compared with those conducted by other tools. This is for numerically testing the adopted procedures and internal coding developed in the tool . RETScreen and eQuest have been used for simulating PV energy generation and energy consumption, respectively. The comparison results show that the difference between both values in different cases varies from 1.9% to 4.9%, which is an accepted range. Second, a validation of the usefulness of the computational method/tool was conducted through a questionnaire to BIPV experts using the generated alternatives. The questionnaire aims to assess and possibly rank a set of alternatives generated by the tool for a specific case by experts, so their inputs can be compared with the tool rank to assess the usefulness of the tool to BIPV designers. Accordingly, a questionnaire was designed using the base-case of office buildings in Egypt (case 1 in the previous section 7), and different 24 generated BIPV alternatives have been divided into 6 groups (4 alternatives in each) in order to facilitate the selection and ranking by experts. Selected experts minimally should be affiliated to BIPV, PV, solar energy, renewable energy or energy efficiency fields with adequate years of experience (5 years minimum). 15 positive responses were received, and they were classified to: BIPV / PV experts (group (A)); solar / renewable energy experts (group (B)), and other experts (e.g., in energy efficiency field) (group (C)).

By analyzing the inputs of experts, Figure 10 shows that the percentages of match between the tool and experts' inputs regarding the selection of the top alternatives were 63%, 49% and 30% based on groups (A), (B) and (C), respectively, while the percentages of match regarding the ranking of all the alternatives were 53%, 49% and 31% based on groups (A), (B) and (C), respectively. On the other hand, the selections in each group of alternatives were analyzed. A lot of similarities and few differences between the tool and experts' selections were found in the majority of alternatives’ groups. Group 1 (shown in appendix A) presents some differences between the tool and the expert ranking, while the majority of the other groups show a significant similarity between them. For example, 60% of experts'

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selections went to alternative (d) in group 1, while it is the second alternative based on the tool selection. This may be because group 1 is one of the hardest groups to evaluate since it has very close performance as that illustrated before. Group 3 has significant difference similar to group 1. While group 2 shows that the majority of expert selections (80%) went to alternatives (b) and (a), these alternatives were selected by the tool as the first and second top alternatives, respectively. The similarity in selections in groups 4, 5 and 6 are 71%, 71%, 67%, respectively.

9. Conclusions This study presents an optimization method and design tool that can help develop the best BIPV alternatives from a given building envelope design with specified design targets/criteria. A detailed framework of an optimization method has been developed to generate better BIPV alternatives for a given building envelope under the developed shape grammar rules. Mandatory and optional design criteria were provided. A computational tool was established to perform the proposed framework computationally via a friendly Graphical User Interface (GUI). The main outputs for each optimized alternative include: (a) the floor shape in a given 2D drawing; (b) its best position in the given layout; (c) the considered architectural priority as specified as an input such as symmetry, view consideration, shape 2D direction or others; (d) the final energy consumption after considering PV performance; (e) related PV generation and payback period; and (f) PV positions on surfaces. The tool has been linked to the GenOpt tool for optimizing the input parameters within specified ranges to achieve minimum energy consumption. Applications on commercial buildings in Egypt were presented to demonstrate the proposed method and relevant tool. The improvement in the 12 cases varies from 3.1% to 20.5% in net energy consumption; it varies among the cases due to specific required inputs (e.g. the modification level and others), while the cases requiring other architectural preferences have also been improved based on that requirement. The validation of the significance of the proposed method/tool has been conducted. The net energy consumption calculated by this tool shows a marginal variance from other simulation tools. A questionnaire aimed at assessing and possibly ranking a set of alternatives generated from the tool has been distributed to BIPV experts, so that their inputs can be compared with the tool ranking to assess

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its usefulness to BIPV designers. Overall, an acceptable match between the subjective votes and the computer selections was obtained.

The study provides BIPV designers a method and tool to produce and compare different BIPV designs to their base designs based on their design preferences and criteria. The study can be extended to include other varieties, options and building envelope features, such as more available modifications (e.g., facade tilting), details (e.g., positions of windows), treatments (e.g., twisting, revolving, etc,), PV modules, architectural preferences (e.g., symmetry and self-shading), and/or building types. This will generate accordingly different sets of alternatives in shapes and performances. The established approach can be extended to start the generation processes from the site inputs rather than the building; this will lead to the development of a new urban and architectural design process for BIPV with more architectural creativity and design principles being used. The tool will be updated in the following versions to include a 3D GUI environment (e.g., via a SketchUp interface) with more intelligent and interactive features. Appendices Two groups of alternatives used for validating the tool and their related details (as examples); tools and experts’ ranking of alternatives in each group are detailed.

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References ASHRAE (2007).ANSI/ASHRAE/IESNA Standard 90.1-2007.Final Qualitative Determination. Autodesk (2014). Ecotect Analysis Software Homepage. Available at http://usa.autodesk.com/ecotect-analysis. Accessed 20 Oct. 2012. Choudhary R, Augenbroe G, Gentry R, Hu H (2008). Simulationenhanced prototyping of an experimental solar house. Building Simulation, 1: 336–355. Caldas LG, Norford LK (2003). Genetic algorithms for optimization of building envelope. Journal of Solar Energy Engineering 125:343–351. Capeluto G, Yezioro A, Bleiberg T, Shaviv E (2005). From computer models to simple design tools: Solar rights in the design of urban streets. In: Proceedings of 9th IBPSA International Conference, Montreal, Canada. DOE-2 (2014) DOE-2 based software homepage, eQuest. Available via http://www.doe2.com/equest.Accessed at 20 Oct. 2014. EIA (2013) International Energy Statistics, Electricity Generation. Available via: http://www.eia.gov/tools/faqs. Accessed at 16 Oct. 2014. ES (2015).Electrical Supplies. Available via: http://www.sourcingelectricals.com/300W-mono-like-solar-module-solar-panel-PVmodule-30033740/. Accessed at 15 Feb 2015. Gips J (2012) shape grammars. Available via http://www.shapegrammar.org. Accessed at 20 Oct. 2014. Granadeiro V., Duarte J.P., Correia J.R. and Leal V.M. (2013). Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation, Automation in Construction. 32: 196–209. Halatsch, J, Kunze, A, Schmitt, G 2008. Using Shape Grammars for Master Planning. In J.S. Gero (ed), Design Computing and Cognition DCC’08, Springer-Verlag, Berlin, pp. 655-773. Hwang T, Kang S, Kim JT (2012). Optimization of the building integrated photovoltaic system in office buildings–Focus on the orientation, inclined angle and installed area. Energy and Buildings, 42: 92–104. Kampf JH, Robinson D (2010). Optimization of building form for solar energy utilization using constrained evolutionary algorithms. Energy and Buildings 42: 807–814. Kitchley JJL, Srivathsan A (2014). Generative methods and the design process: A design tool for conceptual settlement planning. Applied Soft Computing 14: 634–652. Wall M, Probst MCM, Roecker C, Dubois M, Horvat M, Jorgensen OB, Kappel K (2012). Achieving Solar Energy in ArchitectureIEA SHC Task 41. Energy Procedia 30: 1250-1260. Mathworks(2015). MATLAB software. Available via http://www.mathworks.com. Accessed at 25 Aug. 2015. Ministry of Electricity and Energy(2011). Annual Report. Cairo, Egypt: Ministry of Electricity and Energy. NREL (2014) System Advisor Model, SAM.Available via https://sam.nrel.gov.Accessed at 20 Oct. 2014. Ouarghi R, Krarti M (2006). Building Shape Optimization Using Neural Network and Genetic Algorithm Approach. ASHRAE Transactions 112: 484-491. Ruiz-Montiel M, Boned J, Gavilanes J, Jimenez E, Mandow L, Perez-de-la-Cruz J (2013). Design with shape grammars and reinforcement learning. Advanced Engineering Informatics. 27(2):230–245. Stiny, G. (1980). Introduction to shape and shape grammars. Environment and Planning B: Planning and Design 7(3), 343-351. Sui J, Munemoto J (2007).Shape Study on a Green Roof Integrated Photovoltaic System for Bi-objective Optimization of Investment Value and CO2 Emission. Asian Architecture and Building Engineering 6: 307-314. Topaloğlu B (2003). Solar envelope and form generation in architecture. Master Thesis, Middle East Technical University, Turkey. Tuhus-Dubrow D, Krarti M (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Energy and Buildings 45: 1574–1581. Youssef AMA, Zhai Z, Reffat, RM (2015). Design of Optimal Building Envelopes with Integrated Photovoltaics.Building Simulation, Building Simulation 8: 353–366. Youssef AMA, Zhai Z, Reffat, RM (2016). Genetic Algorithm Based Optimizationfor Photovoltaics Integrated Building Envelope. Energy and Buildings 127: 627–636.

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You W, Qin M, Ding W (2013). Improving building facade design using integrated simulation of daylighting, thermal performance and natural ventilation. Building Simulation, 6: 269–282.

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Figure 1: Examples of different optimizations of building envelopes to maximize the PV integration impact via two phases.

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Figure 2: Examples of the SG theory and its rules' applications (Gips, 2012).

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Figure 3: The proposed optimization framework

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Figure 4: The developed main rules of SG for optimizing building shapes: these rules can be applied with different ratios and in different orientations.

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Figure 5: Different examples of applying the developed SG rules, the relations between them and their corresponding classifications: (a) Obtaining different levels of modifications; (b) Considering a specific view or shape ratios; (c) Different alternatives can be obtained using the order of applying rules; (d) Switching between shapes; and (e) Applying some rules may produce inapplicable and inappropriate shapes.

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Figure 6: The developed SG rules for optimizing alternatives' surfaces in different orientations: (a) SG rules in different orientations; (b) A magnification of the rules to present the ratios of used ones; and (c) A explained example (best triangle protrusion ratio in 270 degrees from azimuth).

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Figure 7: The base-case application using the developed computational framework: a) the main inputs and details of the base case to be optimized; b) 12 optimized alternatives using different additional criteria; and c) the details of the 12 introduced alternatives.

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Figure 8: The best generated solutions in the introduced 12 cases using the established tool

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Figure 9: The improvements in net energy consumption of the generated alternatives in the introduced 12 cases compared to their reference cases.

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Figure 10: The percentages of similarity between the tool outputs and expert inputs

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Table 1: The analyses and classifications of the developed SG rules in Phase 1

5E 4N 4W 4S

3E, 4E, 5E

* * *

* * *

* * *

* * *

* * *

* * *

* * *

* * *

3S, 4S, 5S 3W, 4W, 5W 3N, 4N, 5N -

-

* * *

* * *

* * *

* * *

* * *

* * *

* * *

* * *

* * *

* * *

* * *

3E, 4E, 5E

* * * * * * * *

* * * * * * * * * * * * * * * *

3S, 4S, 5S 3W, 4W, 5W 3N, 4N, 5N 1E, 1E*, 3E, 3S, 5E 1S, 1S*, 3S, 3W, 5S 1W,1W*, 3W, 4W, 5W 1N, 1N*, 3N, 3E, 5N -

Central

* * *

* * *

Linear

* * *

West

* * *

Shape

East

South

High modifications

Medium modifications

length: width = more than 2:1 length: width = less than 1:2 Low modifications

U-shape, T-

Z-shape

* * *

3N, 4N, 5N

4E, 4S, 3N, 4N,

* * * *

N: North *

L-shape, shape H-shape

3W, 4W, 5W

North

-

4E 3N 3W 3S

2E 1N* 1W 1S* 1E* 1N 1W 1S *

3E, 4E, 5E

3E 2N 2W 2S

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 R40 R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52 R53 R54 R55 R56 R57 R58 R59 R60 R61 R62 R63 R64 R65 R66 R67 R68 R69 R70 R71 R72

3S, 4S, 5S

5N 5W 5S

GROUP 5

GROUP 4

GROUP 3

GROUP 2

GROUP 1

1E

Inapplicable/ Not recommended rules to be applied after

Classification of SG rules in phase 1 in terms of the related criteria Modificatio View Limitations n Level Consideration

S: South

Recommended

W: West

E: East Applicable

R: Rule

R(i): Rule number (i)

Not recommended

Not Applicable

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Table 2: The improvement on PV generation with various treatment ratios and orientation

Perpendicular surfaces' protrusions

V1 (4x, 4y) V2 (2x, 4y) V3 (4x, 3y) V4 (2x, 3y) V5 (4x, 2y) V6 (2x, 2y) V7 (4x, 1y) V8 (4x, 1y) V1 (4x, 4y) V2 (3x, 4y) V3 (2x, 4y) V4 (1x, 4y) V5 (0x, 4y) V6 (4x, 3y) V7 (3x, 3y) V8 (2x, 3y) V9 (1x, 3y) V10 (0x, 3y)

V12 (3x, 2y)

Triangular protrusions

V13 (2x, 2y) V14 (1x, 2y) V15 (0x, 2y) V16 (4x, 1y) V17 (3x, 1y) V18 (2x, 1y) V19 (1x, 1y) V20 (0x, 1y)

V3 (4x, 3y) V4 (2x, 3y)

54.3% 65.8% 52.2% 42.6% 33.3% 30.0% 24.2%

V5 (4x, 2y)

55.7% 63.3% 51.1% 41.6% 32.4% 30.8% 26.6%

V6 (2x, 2y)

54.3% 65.8% 52.2% 42.6% 33.3% 30.0% 24.2%

V7 (4x, 1y)

55.7% 63.3% 51.1% 41.6% 32.4% 30.8% 26.6%

V8 (4x, 1y)

54.3% 65.8% 52.2% 42.6% 33.3% 30.0% 24.2%

V2 (2x, 4y)

315

300

285

270

255

240

225

210

195

180

165

150

135

120

105

90

75

21.2 % 22.4 % 21.2 % 38.6% 39.2% 31.1% 25.7% 19.8% 18.3% 14.1% 11.1% 8.6% 20.7% 10.7% 8.4% 5.9% 4.0% 3.5% 7.0% 15.5% 30.0% 22.4 % 34.3% 32.9% 26.7% 22.8% 18.9% 18.3% 16.4% 14.8% 13.6% 23.4% 15.3% 13.0% 11.1% 9.4% 8.5% 9.3% 14.7% 26.0% 21.2 % 38.6% 39.2% 31.1% 25.7% 19.8% 18.3% 14.1% 11.1% 8.6% 20.7% 10.7% 8.4% 5.9% 4.0% 3.5% 7.0% 15.5% 30.0% 22.4 % 34.3% 32.9% 26.7% 22.8% 18.9% 18.3% 16.4% 14.8% 13.6% 23.4% 15.3% 13.0% 11.1% 9.4% 8.5% 9.3% 14.7% 26.0% 21.2 % 38.6% 39.2% 31.1% 25.7% 19.8% 18.3% 14.1% 11.1% 8.6% 20.7% 10.7% 8.4% 5.9% 4.0% 3.5% 7.0% 15.5% 30.0% 22.4 % 28.6% 30.4% 25.6% 21.8% 17.1% 15.8% 13.3% 11.9% 12.9% 16.6% 8.0% 24.7 17.6 12.8 10.6 11.6% 25.9% 50.0% 64.7 % % % % % 38.6% 36.7% 31.1% 25.7% 19.8% 18.3% 15.6% 14.8% 15.7% 18.6% 8.0% 24.7% 17.6% 12.8% 5.0% 14.0% 28.4% 50.0 63.5 % % 48.6% 44.3% 36.7% 29.7% 23.4% 20.8% 18.8% 17.0% 17.9% 20.0% 32.7% 24.7% 17.6% 4.0% 9.9% 16.3% 25.9% 41.0% 47.1 % 58.6% 53.2% 42.2% 35.6% 27.0% 25.0% 21.9% 20.7 20.0% 20.7% 32.7% 24.7% 17.6% 5.4% 9.2% 14.7% 22.4% 34.0% 38.8 % % 75.7 65.8% 52.2% 42.6 33.3 30.0 24.2% 18.5% 14.3% 62.1 32.7 24.7% 17.6% 4.0% 5.7% 16.3 11.2% 39.0% 17.6 % % % % % % % % 27.1% 25.3% 22.2% 18.8% 14.4% 14.2% 11.7% 11.1% 12.1% 15.9% 8.7% 24.7% 17.6% 12.8% 10.6% 10.9% 25.0% 49.0% 62.4 % 32.9% 31.6% 26.7% 22.8% 18.0% 16.7% 14.8% 13.3% 14.3% 19.3% 9.3% 24.7% 17.6% 12.8% 8.5% 15.5% 25.9% 45.0% 52.9 % 42.9% 40.5% 33.3% 27.7% 21.6% 20.0% 18.0% 17.0% 18.6% 22.1% 10.0% 24.7% 17.6% 5.4% 9.9% 14.7% 23.3% 37.0% 41.2 % 57.1% 50.6% 42.2% 34.7% 27.0% 25.0% 22.7% 20.7% 21.4 24.1% 32.7% 24.7% 17.6% 5.4% 9.2% 13.2% 19.0% 30.0% 32.9 % % 74.3% 64.6% 51.1% 42.6% 33.3% 30.0% 25.0% 20.0% 17.1% 62.1% 32.7% 24.7% 3.3% 5.4% 7.8% 10.9% 15.5% 25.0% 27.1 % 18.6% 17.7% 14.4% 12.9% 10.8% 9.2% 8.6% 7.4% 7.9% 9.7% 6.0% 1.9% 3.0% 64.7 % 20.0% 20.3% 17.8% 14.9% 11.7% 10.8% 9.4% 7.4% 8.6% 10.3% 6.0% 4.0% 9.9% 16.3% 25.9% 41.0% 47.1 % 31.4% 30.4% 25.6% 21.8% 17.1% 15.8% 13.3% 11.9% 12.9% 16.6% 8.0% 3.9% 4.0% 5.7% 16.3% 11.2% 39.0% 17.6 % 48.6% 44.3% 36.7% 29.7% 23.4% 20.8% 18.8% 17.0% 18.6% 20.0% 2.6% 4.0% 7.1% 10.9% 13.8% 21.0% 23.5 % 75.7% 65.8% 52.2% 42.6% 33.3% 30.0% 24.2% 18.5% 14.3% 62.1% 1.3% 0.6% 2.6% 4.0% 5.7% 8.5% 12.1% 18.0% 18.8 % 22.9% 17.7% 16.7% 13.9% 9.9% 9.2% 7.8% 5.9% 6.4% 7.6% 4.7% 11.6% 25.9% 50.0% 64.7 % 11.4% 11.4% 11.1% 8.9% 7.2% 6.7% 6.3% 5.2% 5.0% 6.9% 4.0% 1.9% 3.9% 4.0% 5.7% 16.3% 11.2% 39.0% 17.6 % 17.1% 16.5% 14.4% 12.9% 9.9% 9.2% 7.8% 6.7% 7.1% 8.3% 5.3% 24.7% 2.0% 3.4% 5.7% 8.5% 12.1% 17.0% 17.6 % 31.4% 30.4% 25.6% 21.8% 17.1% 15.8% 13.3% 11.9% 12.9% 16.6% 8.0% 24.7% 2.0% 2.7% 4.3% 5.4% 7.8% 12.0% 12.9 % 75.7% 65.8% 52.2% 42.6% 33.3% 30.0% 24.2% 18.5% 14.3% 62.1% 32.7% 24.7% 2.0% 2.0% 3.5% 4.7% 6.0% 9.0% 9.4% 34.3% 32.9% 26.7% 22.8% 18.9% 18.3 16.4% 14.8 13.6 23.4 15.3 13.0 11.1 9.4% 8.5% 9.3% 14.7% 26.0% % % % % % % % 38.6 39.2% 31.1% 25.7 19.8 18.3% 14.1% 11.1% 8.6% 20.7% 10.7% 8.4% 5.9% 4.0% 3.5% 7.0% 15.5% 30.0 % % % % 34.3% 32.9% 26.7% 22.8% 18.9% 18.3% 16.4% 14.8% 13.6% 23.4% 15.3% 13.0% 11.1% 9.4% 8.5% 9.3% 14.7% 26.0%

55.7 63.3% 51.1% 41.6% % 54.3% 65.8% 52.2% 42.6 % 55.7% 63.3% 51.1% 41.6%

V1 (4x, 4y)

Shades and louvers

The selected treatments and their ratios' variations (RVs)

V11 (4x, 2y)

60

45

Orientation from azimuth

57.6 % 64.7 % 57.6 % 18.5% 14.3% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 11.6% 25.0% 50.0% 64.7 % 23.0% 21.4% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 10.9% 23.3% 48.0% 57.6 % 18.5% 14.3% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 11.6% 25.0% 50.0% 64.7 % 23.0% 21.4% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 10.9% 23.3% 48.0% 57.6 % 18.5% 14.3% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 11.6% 25.0% 50.0% 64.7 %

32.4% 30.8 26.6% 23.0 21.4 62.1 32.7 24.7 17.6 12.8 10.6 10.9% 23.3% 48.0% % % % % % % % % % 33.3 30.0% 24.2% 18.5% 14.3% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 11.6 25.0% 50.0 % % % 32.4% 30.8% 26.6% 23.0% 21.4% 62.1% 32.7% 24.7% 17.6% 12.8% 10.6% 10.9% 23.3% 48.0%

V (i): Ratio Variation of a treatment on a 2D perpendicular module (x,y). For example, V1 (4x, 4y) in Perpendicular surfaces' protrusions refers to variation number (1) which represent 4 modules in direction X and Y as shown in Figure 6 (b).

Top RVs in each treatment

Selected top RV in each treatment due to its top generation average and bigger surfaces' area

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Table 3: PV module for wall and window integrations PV module for solid walls PV module for windows (Blue Solar, 2016) (UPT Solar, (Power World, 2016) 2013) Cell Type Poly Silicon (P-Si) Amorphous Silicon thin film Dimensions (cm) 99.2 * 165 110 * 130 2 Area (m ) 1.64 1.43 Power (W) 240 100 Module Cost ($) 120 70.2 Transparency (%) 0 20 Color Dark Blue Black (Semi Transparent) Weight (kg) 18.5 18

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Table 4: The essential and additional criteria of the proposed optimization and how they can be met using the developed SG rules

Essential criteria These criteria cannot be exempted in the optimization (mandatory)

Components and Internal classification Net energy consumption:  Reducing energy consumption; or  Increasing PV generation; or  Both

Additional criteria These criteria aim to meet user's preferences if any (optional).

Building area boundaries:

Explanations

How it can be achieved

This is the main criterion of this study to be optimized; the final optimized alternatives should have less net energy consumption either by reducing the energy consumption of the initial case or maximizing the PV generation throw its surfaces or both. The building area should not exceed the specified built area on the site, and also the pre-specified area of the initial shape.

This can be achieved by:  applying GA via GenOpt to select the optimal values in envelope variations in the simulation section (e.g. orientation, height, WWR and others).  applying best SG rules in phase 1 in terms of net energy consumption. In phase 1, this can be achieved by applying protrusions and indentations together for obtaining the similar area of the initial shape. This can be tracked either through the rules' application process or calculating the shape area obtained. As illustrated before in Figure 5 (e), SG rules in phase 1 were tracked to avoid inappropriate shapes and nonfunctional dimensions in the generated alternatives.

Shape requirements:  Avoiding discontinues and inappropriate shapes.  Applying better shape ratios.  Conducting functional shape dimensions.

This criterion has been set to ensure that the shape should be architecturally applicable.

Level of modification:  Low modification level  Normal modification level  High modification level

It refers to the modification level of the basic shape to generate alternatives.

This can be achieved using:  As illustrated before in Figure 5 (a), SG rules in phase 1 can be tracked to give three modification levels.  The number of rules to be applied can be used also to achieve the specified level of modification, because the more applied rules on a shape, the higher modification level to be reached. For example, low, normal and high level of modifications can be achieved through the application of 1, 2 and 3 SG rule; respectively.

Level of PV integration:  Low PV integration  Normal PV integration  High PV integration

It refers to the required level of PV integration in the optimized BIPVs. Specifically, requiring low PV integration will merely lead to integrate PV only in the optimal surfaces and orientations, accordingly lower payback period but with lower generation will be received. While requiring high PV integration will allow distributing PV integration in all possible surfaces (not only the optimal ones), this will allow higher PV generation but with higher payback period. Normal PV integration is the middle level between both previous ones. After achieving the architectural applicability as one of the essential criteria, two additional architectural criteria can be focused on: view consideration (towards northern, southern, eastern or western view) and shape 2D direction (linear or central).

As introduced in phase 2, positions (a), (b), (c) and (d) are representing the descending order of PV generation that can be achieved in each treatment. So, low, normal and high PV integration can be achieved by integrating PV modules in position/s (a), ((a) and (b)) and all positions; respectively.

Architectural preferences:  View consideration (towards northern, southern, eastern or western view).  Shape 2D direction (linear or central).

As shown in the previous Figure 5, the relation between the rules in phase 1 can track the generated alternatives to follow each architectural preference.

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Rectangle L-shape U-shape Basic shapes H-shape T-shape Z-shape Any shape Random Optimization Min. E.C. Criteria Max PV generation Min. Net E.C No Modification Low level Normal High PV Low Generation Normal High level Nothing Architectural View Preference Linear Central





The applied selection on the denoted case



or

or









Case 12

Case 11

Case 10

Case 9

Case 8

Case 7

Case 6

Case 5

Case 4

Case 3

Case 2

Case 1

Table 5: Main inputs of the analyzed 12 cases



     

 



 

 



   

 







 









 

 



 



 

 









 



The applied selection on the denoted case and affecting other selections (matched with the same color in the relevant case). A category that has been excluded from the user’s available selections due to another selection (matched with the same color in the relevant case).

Note: 1. Each case is stand alone; no selections in different cases (row cells in the table) are relevant or affect on each other. 2. The previous colors are separated to denote relevant categories in each case, not a gray scale.

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