Enhancing Industrial Buildings’ Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization
Abstract
:1. Introduction
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- Material and Methods (Section 2): detailed descriptions of the main procedural steps and simulation settings.
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- Results (Section 3): presentation of the main findings for each topic addressed.
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- Discussions (Section 4): analysis and comparison of the research outputs with similar studies in the literature.
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- Conclusions (Section 5): summary of the research outcomes, along with an overview of the limitations and potential future developments.
2. Materials and Methods
- At first, the building was accurately modeled in a BIM environment using Revit, based on detailed specifications of the geometry and internal layout retrieved from the original design drawings and on-site inspections. This model represented the starting point for all subsequent applications and assessments in the research.
- The interoperability between Revit and Rhinoceros Grasshopper was exploited to directly import the building’s geometry and apply a series of autonomously drafted Visual Programming Language (VPL) algorithms. These were designed to effectively intertwine the input data and outcomes of the different parts of the script leveraging the multidisciplinary simulation tools. Figure 3 shows the script created by the authors.
- The Ladybug plugin and its components were used to import and analyze the climatic data referring to the Florence area and to conduct solar and radiation analyses. Simulations were carried out for both the summer and winter solstices (21 June and 21 December) considering a time interval between 7:00 a.m. and 6:00 p.m. to align with the working hours of the company occupying the building. To promote simultaneous energy production and consumption, it is advisable to maximize the yield of the photovoltaic system during the operating hours of the machinery and installed equipment. A multi-objective optimization study was carried out using the Octopus plugin [31] to evaluate different BIPV layouts applied to the different building facades. The energy production and initial investment costs were addressed as evaluation criteria. To express both parameters, the total incident radiation and the total area of the panels installed were used as references. The former is key for assessing the productiveness of the installed PV system, while the area of the PV surface was assumed as representative of the costliness of the different configurations evaluated, as it is directly related to the number of panels installed. Solar incident radiation was calculated through the dedicated simulation engine provided by the Ladybug plugin and included in the Octopus-based optimization. The tilt angle (ranging from 0° to 90° with 10° increments) and the distance (ranging from 0.5 m to 1.5 m in 0.20 steps) between consecutive rows of PV modules on each facade were introduced as the variables for the problem (Table 1). The minimum distance was set to avoid the overlapping of panels when considering their perfectly vertical layout. The research was further developed by applying progressive rotations of 10° with respect to the N-S axis for the entire building, considering the PV modules arranged according to the optimized configuration minimizing the panels’ area in winter conditions. This approach aimed to identify the most promising building orientation ranges for implementing BIPV solutions.
- The Honeybee plugin [32] was used to assess the visual comfort conditions within the working environment, and it was exploited to perform daylight simulations considering the daylight factor (DF), daylight autonomy (DA), and the useful daylight illuminance (UDI) as reference parameters. These parameters respectively represent the amount of daylight that penetrates the building interior, the extent to which a space is naturally lit during occupied hours to satisfy the required illuminance conditions, and the percentage of the annual hours during which the illuminance threshold is met. Solar radiation and climate data were derived from those imported using Ladybug, as previously introduced. To account for the different possible configurations of the building’s apertures commonly retrieved in existing manufacturing facilities, alternative configurations were evaluated: windows on each facade arranged in a single row or two rows, and the presence of roof skylights, including a combination of these scenarios. In this case, the simulations were performed considering the entire year and a minimum illuminance level of 300 lux, as prescribed by UNI EN 12464-1 [33] for generic activities in an industrial environment.
- The outputs of daylight illuminance and incident radiation analyses were also included in defining the optimized solar shading system configurations. In this case, different alternative solutions were evaluated using a design-optioneering approach, iterating a series of simulations through the Colibrì plugin [34] for Grasshopper. For this analysis, 0.20 deep external louvres were designed, considering the tilt angle of each blade ranging from 0° to 90° and vertical spacing between consequent slats from 0.20 m to 1 m. The evaluation focused on 60 distinct options, each resulting from variations in the geometrical arrangements achieved through adjustments of 10° in blade tilt and 0.20 m in vertical spacing. These alternatives were assessed for their performance during the summer solstice throughout the occupancy period. The results were explored through the online viewer Design Explorer by Thornton Tomasetti [35], which allows the comparison and filtering of different configurations tested according to the desired outputs or limitations assigned to the design variables. For the study here presented, the amount of solar incident radiation and the internal UDI average values were considered driving design factors, aiming for an arrangement capable of preventing the influx of excessive solar radiation without compromising adequate natural lighting contribution.
Case Study Building
3. Results
3.1. Optimization of the PV Panels’ Distribution
3.2. Daylight Analysis
3.3. Optimization of Solar Shading Systems
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Element | Dimensions | |
---|---|---|
PV panel | 1.09 m | 0.58 m |
Louvres blades | 1.10 m | 0.20 m |
Parameter | Range | Increment |
PV vertical spacing | 0.5–1.5 m | 0.20 m |
PV tilt angle | 0–90° | 10° |
Louvres vertical spacing | 0.20–1 m | 0.20 m |
Louvres tilt angle | 0–90° | 10° |
Parameter | Value |
---|---|
Heating setpoint | 18 °C |
Occupancy time | 8:00–17:00 |
Occupancy | 0.01 people/m2 |
Metabolic rate | 167 W/person |
Natural ventilation flow rate | 0.77 m3/s |
Airtightness | 0.20 ac/h |
Sheltering coefficient | 0.7 |
Parameter | Value |
---|---|
Maximum power | 110 W |
Maximum power voltage | 19.6 V |
Maximum power current | 5.66 A |
Module efficiency | 17.29% |
Operating temperature | −40 °C to 85 °C |
Type of solar cell | Monocrystalline silicon cells |
Cell size | 166 × 83 mm |
No. of cells | 36 |
Temperature coefficient of Voc | −0.28%/°C |
Temperature coefficient of Isc | 0.02%/°C |
Latitude | Longitude | Climate Zone | Heating Period | HDD [K/d] | Gh [kWh/m2a] | Dh [kWh/m2a] | Bn [kWh/m2a] | Ta [°C] | Td [°C] | Ws [m/s] |
---|---|---|---|---|---|---|---|---|---|---|
43.34° N | 11.52° E | D | 1/11–15/04 | 2041 | 1447 | 629 | 1496 | 15 | 7.9 | 2.8 |
Material | Thickness (m) | Conductivity (W/m·K) | Specific Heat (J/kg·K) | Density (kg/m3) |
---|---|---|---|---|
Precast concrete | 0.02 | 2.07 | 1000 | 2400 |
Insulation material (EPS) | 0.045 | 0.04 | 1450 | 15 |
Precast concrete | 0.02 | 2.07 | 1000 | 2400 |
Material | Thickness (m) | Conductivity (W/m·K) | Specific Heat (J/kg·K) | Density (kg/m3) |
---|---|---|---|---|
Asbestos–cement tiles | 0.01 | 0.6 | 1000 | 1800 |
Glass wool | 0.06 | 0.04 | 1030 | 12 |
Air gap | 0.55 | - | - | - |
Asbestos–cement tiles | 0.01 | 0.6 | 1000 | 1800 |
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Banti, N.; Ciacci, C.; Bazzocchi, F.; Di Naso, V. Enhancing Industrial Buildings’ Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization. Solar 2024, 4, 401-421. https://doi.org/10.3390/solar4030018
Banti N, Ciacci C, Bazzocchi F, Di Naso V. Enhancing Industrial Buildings’ Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization. Solar. 2024; 4(3):401-421. https://doi.org/10.3390/solar4030018
Chicago/Turabian StyleBanti, Neri, Cecilia Ciacci, Frida Bazzocchi, and Vincenzo Di Naso. 2024. "Enhancing Industrial Buildings’ Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization" Solar 4, no. 3: 401-421. https://doi.org/10.3390/solar4030018
APA StyleBanti, N., Ciacci, C., Bazzocchi, F., & Di Naso, V. (2024). Enhancing Industrial Buildings’ Performance through Informed Decision Making: A Generative Design for Building-Integrated Photovoltaic and Shading System Optimization. Solar, 4(3), 401-421. https://doi.org/10.3390/solar4030018