Increasing population causes Energy consumption and environmental pollution. It is essential to consider renewable forms of energy, especially solar power, to reduce energy consumption. This requires attention to energy issues in the early stages of urban design and practical and creative solutions for more efficient use of this type of energy. This study aims at calculating the annual solar radiation at a city scale through a novel process and methodology. In this regard, artificial intelligence algorithms and satellite data can help maximize the amount of sunlight in neighborhoods and urban blocks in neighborhood units during the development process. In the simulation process, location, and optimization of the urban form, it is necessary to consider the limitations and resources for field study and simulation of urban blocks. Therefore, in this study, Farhangian neighborhood in phase 1 of Kermanshah, Iran, which has a good level of structural diversity and lends itself to field studies, was selected and studied at neighborhood and urban block scales. The case study indicates the significant role of calculating and optimizing the patterns of urban blocks to achieve maximum solar energy. Estimates at different levels show that urban block variables effectively access solar radiation energy and, given various scales of development - from macro-scale spatial planning to micro-scale local design - can improve energy intake by 3 to 5 percent. Accordingly, the results show that to accelerate the calculation of energy at the planning scale, the use of 2.5D locating model and 3D optimization contribute to achieving the maximum or minimum solar radiation, respectively. On the other hand, this method can be used to organize calculations and planning for maximum absorption of solar radiation at different stages of development.
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Abd Alla, S., Bianco, V., Tagliafico, L. A., & Scarpa, F. (2020). An innovative approach to local solar energy planning in Riva Trigoso, Italy. Journal of Building Engineering, 27. https://doi.org/10.1016/j.jobe.2019.100968
Amado, M., & Poggi, F. (2012). Towards solar urban planning: A new step for better energy performance. Energy Procedia, 30, 1261-1273.
Amado, M., & Poggi, F. (2014a). Solar energy integration in urban planning: GUUD model. Energy Procedia, 50, 277-284.
Amado, M., & Poggi, F. (2014b). Solar Urban Planning: A Parametric Approach. Energy Procedia, 48, 1539-1548. https://doi.org/10.1016/j.egypro.2014.02.174
Amado, M., Poggi, F., & Amado, A. R. (2016). Energy efficient city: A model for urban planning. Sustainable Cities and Society, 26, 476-485. https://doi.org/10.1016/j.scs.2016.04.011
Amado, M., Poggi, F., Ribeiro Amado, A., & Breu, S. (2017). A Cellular Approach to Net-Zero Energy Cities. Energies, 10(11). https://doi.org/10.3390/en10111826
Besussi, E., Chin, N., Batty, M., & Longley, P. (2010). The structure and form of urban settlements. In Remote sensing of urban and suburban areas (pp. 13-31). Springer.
Bizjak, M., Žalik, B., & Lukač, N. (2015). Evolutionary-driven search for solar building models using LiDAR data. Energy and Buildings, 92, 195-203. https://doi.org/10.1016/j.enbuild.2015.01.051
Carnieletto, L., Ferrando, M., Teso, L., Sun, K., Zhang, W., Causone, F., Romagnoni, P., Zarrella, A., & Hong, T. (2021). Italian prototype building models for urban scale building performance simulation. Building and Environment, 192, 107590.
Cerezo Davila, C. (2017). Building archetype calibration for effective urban building energy modeling Massachusetts Institute of Technology].
Chen, K., & Norford, L. (2017). Evaluating Urban Forms for Comparison Studies in the Massing Design Stage. Sustainability, 9(6). https://doi.org/10.3390/su9060987
Chen, K. W., & Norford, L. (2017). Developing an Open Python Library for Urban Design Optimisation-Pyliburo. Building Simulation,
Dogan, T. (2015). Procedures for automated building energy model production for urban and early design Massachusetts Institute of Technology].
Eastman, C. M., Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. John Wiley & Sons.
EnergyPlus_Development_Team. (2020). Weather Data for EnergyPlus. (File, EnergyPlus Weather), https://energyplus.net/weather.
Fernández-Ahumada, L. M., Ramírez-Faz, J., López-Luque, R., Márquez-García, A., & Varo-Martínez, M. (2019). A Methodology for Buildings Access to Solar Radiation in Sustainable Cities. Sustainability, 11(23). https://doi.org/10.3390/su11236596
Freitas, S., Catita, C., Redweik, P., & Brito, M. C. (2015). Modelling solar potential in the urban environment: State-of-the-art review. Renewable and Sustainable Energy Reviews, 41, 915-931. https://doi.org/10.1016/j.rser.2014.08.060
Fu, P., & Rich, P. (2000). The solar analyst 1.0 user manual. Helios Environmental Modeling Institute, 1616.
Gadsden, S., Rylatt, M., Lomas, K., & Robinson, D. (2003). Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS. Energy and Buildings, 35(1), 37-48.
Groppi, D., de Santoli, L., Cumo, F., & Astiaso Garcia, D. (2018). A GIS-based model to assess buildings energy consumption and usable solar energy potential in urban areas. Sustainable Cities and Society, 40, 546-558. https://doi.org/10.1016/j.scs.2018.05.005
Horvat, M., & Wall, M. J. R. T. (2012). Solar design of buildings for architects: Review of solar design tools. 41.
Kanters, J., Horvat, M., & Dubois, M.-C. (2014). Tools and methods used by architects for solar design. Energy and Buildings, 68, 721-731.
Kanters, J., Wall, M., & Kjellsson, E. (2014). The solar map as a knowledge base for solar energy use. Energy Procedia, 48, 1597-1606.
Krier, L. (1978). Fourth Lesson: Analysis and project of traditional urban block. Lotus International, 19, 42-54.
Liang, J., Gong, J., Li, W., & Ibrahim, A. N. (2014). A visualization-oriented 3D method for efficient computation of urban solar radiation based on 3D–2D surface mapping. International Journal of Geographical Information Science, 28(4), 780-798.
Lila, A., Jabi, W., & Lannon, S. (2021). Predicting solar radiation with Artificial Neural Network based on urban geometrical classification.
Lobaccaro, G., & Frontini, F. (2014). Solar energy in urban environment: how urban densification affects existing buildings. Energy Procedia, 48, 1559-1569.
Lobaccaro, G., Frontini, F., Masera, G., & Poli, T. (2012). SolarPW: A new solar design tool to exploit solar potential in existing urban areas. Energy Procedia, 30, 1173-1183.
Lobaccaro, G., Lisowska, M. M., Saretta, E., Bonomo, P., & Frontini, F. (2019). A Methodological Analysis Approach to Assess Solar Energy Potential at the Neighborhood Scale. Energies, 12(18). https://doi.org/10.3390/en12183554
Machete, R., Falcão, A. P., Gomes, M. G., & Moret Rodrigues, A. (2018). The use of 3D GIS to analyse the influence of urban context on buildings' solar energy potential. Energy and Buildings, 177, 290-302. https://doi.org/10.1016/j.enbuild.2018.07.064
Makki, M., Showkatbakhsh, M., Tabony, A., & Weinstock, M. (2019). Evolutionary algorithms for generating urban morphology: Variations and multiple objectives. International Journal of Architectural Computing, 17(1), 5-35.
Martins, T. A., Adolphe, L., & Bastos, L. E. (2014). From solar constraints to urban design opportunities: Optimization of built form typologies in a Brazilian tropical city. Energy and Buildings, 76, 43-56.
Mert, Y., & Saygın, N. (2016). Energy efficient building block design: An exergy perspective. Energy, 102, 465-472. https://doi.org/10.1016/j.energy.2016.02.121
Mohajeri, N., Upadhyay, G., Gudmundsson, A., Assouline, D., Kämpf, J., & Scartezzini, J.-L. (2016). Effects of urban compactness on solar energy potential. Renewable Energy, 93, 469-482. https://doi.org/10.1016/j.renene.2016.02.053
Noorian, A. M., Moradi, I., & Kamali, G. A. (2008). Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces. Renewable Energy, 33(6), 1406-1412.
Pili, S., Desogus, G., & Melis, D. (2018). A GIS tool for the calculation of solar irradiation on buildings at the urban Scale, based on Italian standards. Energy and Buildings, 158, 629-646. https://doi.org/10.1016/j.enbuild.2017.10.027
Poon, K. H., Kämpf, J. H., Tay, S. E. R., Wong, N. H., & Reindl, T. G. (2020). Parametric study of URBAN morphology on building solar energy potential in Singapore context. Urban Climate, 33. https://doi.org/10.1016/j.uclim.2020.100624
Rahbar, M., MahdaviNejad, M., Bemanian, M., & Davaie-Markazi, A. (2020). Artificial neural network for outlining and predicting environmental sustainable parameters. Journal of Sustainable Architecture and Urban Design, 7(2), 169-182.
Ratti, C., Baker, N., & Steemers, K. (2005). Energy consumption and urban texture. Energy and Buildings, 37(7), 762-776.
Redweik, P., Catita, C., & Brito, M. (2013). Solar energy potential on roofs and facades in an urban landscape. Solar Energy, 97, 332-341. https://doi.org/10.1016/j.solener.2013.08.036
Rodríguez-Álvarez, J. (2016). Urban Energy Index for Buildings (UEIB): A new method to evaluate the effect of urban form on buildings' energy demand. Landscape and Urban Planning, 148, 170-187. https://doi.org/10.1016/j.landurbplan.2016.01.001
Roudsari, M. S. (2017). What is Ladybug Tools. Availabe online: http://www.ladybug.tools/(accessed on 18 November 2017).
Sarralde, J. J., Quinn, D. J., Wiesmann, D., & Steemers, K. (2015). Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London. Renewable Energy, 73, 10-17. https://doi.org/10.1016/j.renene.2014.06.028
Sattrup, P. A., & Strømann-Andersen, J. (2013). Building typologies in northern European cities: daylight, solar access, and building energy use. Journal of Architectural and Planning Research, 56-76.
Scott, D. (2010). Grasshopper generative modeling for Rhino. In Computer software (Version 6) McNeel, Robert. http://www.grasshopper3d.com
Shakibamanesh, A., & Veisi, O. (2021). Designing Sustainable Urban Blocks: An Effort to Optimizing 3D Form and Achieving Maximum Amount of Solar Radiation. In Advances in Urbanism, Smart Cities, and Sustainability (pp. 405-429). CRC Press.
Shi, Z., Fonseca, J. A., & Schlueter, A. (2017). A review of simulation-based urban form generation and optimization for energy-driven urban design. Building and Environment, 121, 119-129. https://doi.org/10.1016/j.buildenv.2017.05.006
Tuhus-Dubrow, D., Krarti, M. J. B., & environment. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. 45(7), 1574-1581.
Vermeulen, T., Knopf-Lenoir, C., Villon, P., & Beckers, B. (2015). Urban layout optimization framework to maximize direct solar irradiation. Computers, Environment and Urban Systems, 51, 1-12.
Vermeulen, T., Merino, L., Knopf-Lenoir, C., Villon, P., & Beckers, B. (2018). Periodic urban models for optimization of passive solar irradiation. Solar Energy, 162, 67-77. https://doi.org/10.1016/j.solener.2018.01.014
Wall, M., Snow, M., Dahlberg, J., Lundgren, M., Lindkvist, C., Lobaccaro, G., Siems, T., Simon, K., & Probst, M. C. M. (2017). Urban planning for solar energy-IEA SHC TASK 51. ISES Solar World Conference 2017, SWC 2017 and 5th International Conference on Solar Heating and Cooling Conference for Buildings and Industry 2017, SHC 2017,
Wang, J. (2010). The form of clean energy neighborhoods: how it is guided and how it could be Massachusetts Institute of Technology].
Wijeratne, W. P. U., Yang, R. J., Too, E., & Wakefield, R. (2019). Design and development of distributed solar PV systems: Do the current tools work? Sustainable Cities and Society, 45, 553-578.
Xu, X., Wu, Y., Wang, W., Hong, T., & Xu, N. (2019). Performance-driven optimization of urban open space configuration in the cold-winter and hot-summer region of China. Building Simulation,
Yi, Y. K., & Kim, H. J. S. E. (2015). Agent-based geometry optimization with Genetic Algorithm (GA) for tall apartment's solar right. 113, 236-250.
Zhang, J., Xu, L., Shabunko, V., Tay, S. E. R., Sun, H., Lau, S. S. Y., & Reindl, T. (2019). Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density City. Applied Energy, 240, 513-533. https://doi.org/10.1016/j.apenergy.2019.02.033
Zhang, S., Li, X., She, J., & Peng, X. (2019). Assimilating remote sensing data into GIS-based all sky solar radiation modeling for mountain terrain. Remote Sensing of Environment, 231, 111239.
Zhang, Y., & Liu, C. (2021). Parametric Urbanism and Environment Optimization: Toward a Quality Environmental Urban Morphology. International Journal of Environmental Research and Public Health, 18(7), 3558.
Zhang, Y., & Schnabel, M. A. (2018). Parametric Thinking in Form-Based Code Evaluation. International Journal of Environmental Science & Sustainable Development, 3(2), 89-99.
Zhu, R., Wong, M. S., You, L., Santi, P., Nichol, J., Ho, H. C., Lu, L., & Ratti, C. J. R. E. (2020). The effect of urban morphology on the solar capacity of three-dimensional cities.
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