Efficient energy planning and design denote the relationship between land use and building design. Principles of efficient energy planning involve the systematic examination of the city in terms of three scales, i.e., settlement characteristics, building block characteristics, and building characteristics when deciding on land use. Therefore, it is necessary to consider ideas for mitigating the effects of climate change and ensuring the efficient and effective use of energy (Mert & Saygın, 2016). The related literature shows that urban geometry and energy consumption are closely related. In this regard, previous studies have examined in detail the relationship between urban morphological parameters and the theoretical energy efficiency and energy heat generated by the spatial pg. 66 configuration of cities. A common theme in global approaches to the city and energy revolves around the sun's potential as the most significant energy source available to all urban areas (Amado et al., 2016).

The process of energy efficiency or, more broadly, improving the sustainability of building quota is not just a matter of technology optimization but also involves decision-makers, investors, and citizens. To promote and regulate such a large and complex process in renovation projects, local authorities, i.e., decision-makers, must have the required knowledge and tools to design plans or programs that integrate the energy model into more comprehensive approaches to urban renovation (Pili et al., 2018). In addition, the analysis of constraints on an urban scale makes it possible to determine more realistically the solar potential of construction surfaces. However, the studies of typological factors and their sensitivity and relationship with solar technology can lead to different interpretations by introducing weight indicators for different geometry, typology, and construction constraints that affect the integrity of the solar energy system. Despite using solar energy in cities to create sustainability in housing facilities, there is no related commitment in construction. Similarly, the amount of solar radiation is usually not taken into account in urban planning decisions. It is essential to know the solar radiation levels reaching those parts of the building used to install solar panels or heat collectors to formulate energy efficiency measures in new buildings (Fernández-Ahumada et al., 2019).

The methods and tools used in the initial design phase should support architects and planners in decisions leading to solar buildings and help further develop and evaluate various solar technologies in the construction phase (Horvat & Wall, 2012). Tool constraints include model configuration and model simulation time for urban projects indicating the need for different solutions and simplifications to increase the speed and accuracy of such models (Dogan, 2015). Urban morphology significantly impacts solar potential, daylight, and natural ventilation. Hence, comprehensive planning largely determines the inherent environmental performance and its blocks. Architectural and urban planners should develop a wide range of related guidelines and simulations in their design process. This parametric study, performed by simulation tools in a process-oriented approach at two different scales, is probably the first step to this end because it analyzes the different types of morphological structure and urban blocks and their potential contribution to locally produced energy. This study seeks to find a comprehensive solution to quantify the role of solar energy as a source of renewable energy in various urban morphologies. In addition, it investigates the possibility of achieving the maximum amount of solar radiation through the use of the 2.5D measurement method and three-dimensional data, especially data on the urban block scale and a combination of these two data types.

Urban areas have expanded widely in recent decades, and the rise will probably continue in the coming decades. Many countries provide excellent opportunities for local generation and energy use, thereby minimizing energy loss or transfer (Mohajeri et al., 2016). Typology is a tool admired by urban designers. Mapping the future of cities in Europe from the late 1960s to the 1970s encouraged Krier (Krier, 1978) to use typological studies to guide design, reintegrate fragmented parts of the town, and emulate the best pre-industrial cities. However, as people's lifestyle changes much more dramatically than in the past, the typology of the city will no longer host it forever, and the elements of the urban form are constantly changing and getting replaced. Thus, new typologies emerge, reflecting people's unique lifestyles, though not entirely invented. On the other hand, the building plan usually determines the activities and programs inside the building. After decades of implementation and critique of zoning, the mixed-use approach has become a common strategy for programs on a regional scale (Shi et al., 2017). These forms should be considered in line with climate programs and part of a long-term plan based on environmental needs.

Using the 3D model for analyzing solar radiation due to different limitations, such as large scales and solar direct radiation in urban areas, is always not possible. Also, selecting an algorithm for calculating solar radiation by comparing different models is possible, Because computational algorithms adopt different models of sky vision and sources for climate data (Noorian et al., 2008). In some cases, the researcher utilizes small-scale analysis and statistical models. While others compare the results provided by more well-known tools for urban-scale solar analysis or with more accurate calculations performed by software for building-level dynamic solar analysis. Computational time and activities, the demand for setting up basic data, and interpreting results are some of the pg. 67 critical issues that can limit the application of these models in areas with high economic and human resources. Building energy performance is influenced by urban geometry, building design, Systems efficiency, and Residents' behavior. Also, these factors are under the control of various actors in the construction sector: urban planners and designers, architects, and system engineers (Ratti et al., 2005). Thus, the calculation in different scales of the cities is divided into three scales: small Scale, middle Scale, and macro Scale.

Architects and urban planners often do not have enough knowledge and time to evaluate solar energy potential during the design process. Therefore, having a technical background and determining the appropriate time for analysis can help them achieve maximum solar potential and encourage them to study more efficient design solutions for integrating solar systems into building coatings (Lobaccaro et al., 2019). Realizing solar energy potential in the urban context requires determining the related criteria and parameters according to specific issues that should be considered (Amado & Poggi, 2014)

This study was analytical and was based on sustainable urban development and documented principles derived by the authors in the two fields of urban design and urban morphology. The aim was to compare the ideal situation with the current situation and to achieve the average height codes in urban blocks for future planning by optimizing the current situation. In addition, to speed up the planning process, the study seeks a connection between two-dimensional solar radiation data and calculated and optimized three-dimensional data. Also, implementing a comprehensive approach to calculating solar radiation at different urban scales was another goal of this study. The process had been operationalized to help urban planners and designers link urban patterns and energy production factors. Then, a customized workflow was designed using the paradigm of parameters and essential indicators at a city scale. A workflow was developed using GIS, parametric modeling, and solar dynamic analysis at various scales based on Figure 1.

Simulation tools were generally divided into three categories: building energy performance analysis tools, microclimate analysis tools, and finally, tools that address neighborhood shape and energy consumption(Wang, 2010). Moreover, solar radiation calculations can be divided into three categories based on the solar radiation model and the tool that classifies the three-dimensional form of the city. These include I) All-in-one model, which couples the modules used for solar radiation treatment with design interfaces or three-dimensional object representation in a single software; II) CAD plugin-based model, which receives plugins from other software capable of performing radiation analysis; and III) GIS-based models (Freitas et al., 2015). Accordingly, this study had divided research modeling into two different phases. In the first phase, designers and planners, who might not be able to provide accurate information about building construction details and performance details in the preliminary planning and design period of the site (Wang, 2010), study a layer of raster geographic information and makes it possible to examine displacement characteristics in the radiation model, such as slope, orientation, and latitude in large areas, on an urban planning scale (macro-scale)(Redweik et al., 2013).

The second stage, which was in the form of small-scale parametric data, marks the beginning of developing a research method with the ultimate goal of calculating solar energy at urban design and building architecture scales. The next step was to understand how to change urban planners' current design styles (mid-scale and micro-scale). To address the shortcomings of existing micro-scale techniques, the hybrid method of urban building energy modeling (UBEMs) has been proposed as a new simulation method, which uses bottom-up modeling with physicsbased simulation techniques. Within UBEM, each building was presented as a thermal model based on the same principles of heat transfer that govern individual building energy models (BEM). Generation of UBEM requires the definition of countless input data for building geometry and a large set of non-geometric energy-related parameters (structures, systems, use patterns, loads, etc.). However, the model processes created for individual buildings can be applied directly to the urban Scale due to the larger model size and unavailability of data. They require the use of various abstraction and simplification methods. Several methods have been proposed for generating building geometry from GIS or LIDAR datasets and converting them to simple thermal models with reasonable simulation time (Cerezo Davila, 2017).

The first step in improving the energy performance of buildings is to study and simulate their behavior. Many energy models and techniques have been developed for this purpose in recent years. However, these models are usually from the building designer's perspective, as they tend to consider buildings in terms of defined entities and ignore the importance of urban-scale phenomena. In particular, the effect of urban geometry on energy consumption is still controversial. One reason for this shortcoming is the problematic nature of modeling complex urban geometry (Ratti et al., 2005).

The study area was the Farhangian neighborhood, phase 1, located in region two of Kermanshah, Iran. This neighborhood is bordered by the Bargh neighborhood from the north, Sarcheshmeh neighborhood from the south, Dieselabad neighborhood from the east, and Taxirani from the west. Farhangian neighborhood, phase 1, is located in a strategic area of the city in terms of location inFigure 2. This neighborhood has excellent potential for vertical expansion and development due to its proximity to the main urban square and is located in the central metropolitan area. Such a development is access to more municipal services and high land prices in these areas. Therefore, creating indicators and criteria for sunlight access in this area is particularly important.

Region 2 of Kermanshah is one of the newly built regions with high economic potential with old patterns due to special blocks. The possibility of vertical development in the region was also increased due to its good access and convenient distance from the city center. In addition, various patterns of blocks can be seen in the area. Therefore, Farhangian neighborhood of Kermanshah was selected as a suitable site for initial analysis. Next, a general analysis of the site was carried out, and 2.5-dimensional radar data and two-dimensional data at the urban block level were reviewed to select index block patterns for final analysis.

Energy calculations at different stages of design require a comprehensive approach. Though Rodríguez-vlvarez adapted thermal and lighting calculations for use at the urban Scale (Rodríguez-Álvarez, 2016). these calculations do not have optimal performance for different scales. On the other hand, various methods and software are used to study energy variables in the analysis and study of urban morphology. Depending on the Scale and complexity of the study, the study of urban morphology was faced with certain complexities. Hence, this analysis can be reduced at large-scale data and up to the building scale. Entering different energy categories further exacerbates this complexity. Therefore, researchers generally studied energy in two general areas. The first area, which includes technology and urban economic models, was investigated via a top-down approach. In contrast, the second area, which was divided into engineering and statistics, involves using a bottom-up method.

Urban building energy modeling is done through main approaches, i.e., "top-down" and "bottom-up"; The top-down approach involves using known energy consumption for a specific area and period (usually annual) and dividing it into sections attributed to particular groups of buildings. The bottom-up approach takes a reverse path and creates models at the Scale of single buildings with the same energy consumption. Then, the results were summarized for all buildings in the complex. While both approaches aim to describe energy consumption, the top-down models were limited because they were trained using historical data on consumption levels, construction conditions, economic indicators, etc. The ability to predict relies very small on changes in the status quo and, therefore, cannot model the consequences of technological advances, changes in construction practices, etc. The bottom-up approach did not have such limitations. Another advantage was that energy consumption could be divided into final uses right from the beginning, and the results could have a high spatial resolution(Cerezo Davila, 2017).

Therefore, analyzing data in the field of energy and the management, calculation, and amount of this energy needs to be studied in various areas of urban modeling. However, studying these areas at different scales is impossible with a single tool to optimize early urban models. The duration of large-scale radiation energy analysis may not be cost-effective in terms of time and cost. Therefore, different tools should be used depending on the process and the area under study. This study provides different models at each level. It analyzes each in terms of management and design studies that can effectively carry out solar radiation analysis within an urban block.

Various methods have been developed for modeling solar radiation that differs mainly in their scattered radiation estimation. Some solar potential estimation methods have been developed In recent years that consider topography from LiDAR data (Bizjak et al., 2015). GIS-based models with the ability to display real-world space for visualization and simulation purposes have been the most powerful. In terms of GIS presentation, there are two ways to obtain a digital surface model (DSM) in an urban environment: i) achieving a set of three-dimensional points allowing the geometric reconstruction of the elements under study, and II) using procedural or parametric modeling (Machete et al., 2018). The most common is three-dimensional models derived from LiDAR through automated algorithms or manually devised with a simple CAD method and building footprints. The input data are used for defining 2.5d raster layers (building heights, facets, slopes, etc.), which, along with other tool settings, form input data for GIS-based solar analysis (Pili et al., 2018). Various methods should be used to assess the solar potential of urban areas. Of these, it has been shown that geographic information systems (GIS) help estimates regional renewable energy potential and practical support for urban-scale energy planning decision-making (Groppi et al., 2018).

The first step in energy analysis was to prepare the maps needed for measurement. There were two ways to receive raster data (DEM). First, radar data taken from sources like the United States Geological Survey can be used. In this case, the map was based on 30*30 big black and white mosaic parcels. The Scale of these maps was so large that the entire neighborhood being investigated was summarized in a few parcels, which was a fundamental problem in analyzing solar radiation data. Solar radiation analysis performs calculations according to these parcels, and each parcel represents a certain amount of radiation, so fewer parcels means less radiation information.

The second mode for calculating radiation was using data generated by point cloud in Google Earth software. To this end, we need to create a point cloud on the desired location based on map data in Google Earth. Then, the cloud point was entered into www.gpsvisualizer.com to add height data to these points (Visualizer, 2018). The website uses the global DEM data to add a third column to the set of issues that show the height code of our intended points. Then, this TEXT data was entered into the GIS environment and converted into points, which were then converted into a DEM file using Point to Raster tool. The advantage of this method was the creation of a raster map with pixel dimensions of 5*5 meters. This was much smaller than the pixels in the previous process, which provides the user with larger and more detailed maps. This model was selected for the accuracy of its operation. Figure 3 summarizes the steps of structural investigations in solar analysis. After the above steps, the data was entered into the Area solar Radiation plugin in the Arcmap tool for research. The output can be seen in Figure 4 andFigure 7.

The diagrams in Figure 4 above show the amount of solar radiation in different seasons of the year based on four variables, each in a particular range based on the radiation amount. Two critical variables in this area, which were significant for this study, were radiation duration and direct radiation.

Comparing different seasons in Table 2 and Figure 5 in the radar and cloud point data shows clearly that the ratio of direct radiation to total radiation in summer and autumn was higher than in the rest of the year. Likewise, natural radiation had a larger share of total radiation than other computational radiations. This was why, in many studies, these two outputs were considered equivalent.

Inspection of the data in Figure 6 shows that the distribution of radiation changes at different times of the year was expected, and statistical tests can examine the changes in the data. On the other hand, it confirms that point cloud data provides higher accuracy and more information and can present on-site radiation information for different points with different values. This can give planners and city managers more decision-making power when planning land use. Therefore, there was a clear need for higher quality satellite data for further research.

The relationship between radiation variables in Figure 5 and Table 3 shows a direct connection between the rate of change at different times of a season in a single region. The points that receive more radiation in a season during a year follow the same pattern in other seasons, and the same way can be used in planning. The study of the annual radiation variable using radar data in Figure 6 shows a significant amount of radiation in Farhangian, phase 1 in Kermanshah. What was essential was the use of satellite data for planning on energy and urban scales. According to the 2.5-dimensional DEM data, the minimum and maximum amounts of radiation energy for the Earth's surface area are 1.4 and 1.6 million kWh, respectively. Therefore, a significant difference can be made in the received radiation by choosing optimal locations.

Annual data on solar radiation in different modes confirmed the importance of solar radiation hours during a year. This data was essential because no technology can use all the solar radiation energy, both in the passive and active areas. Thus, the duration of time when solar energy was available was more important than its amount. Here, the sunshine time was maximum in most parts of the site, as shown in dark blue in Figure 7. The maximum annual number of hours is 4365, suitable for using radiant energy in the study site.

In the study of the amount of annual radiation in the point cloud method, direct radiation was maximum, comprising more than 80% of the total radiation energy. In other words, comparing the radiation time and the amount of direct radiation shows that cloudy days in this region were low, which can further be corroborated by examining climate data.

Examining the radiation diagrams in Figure 9 and Table 5 shows two cases in which the changes in the two graphs follow the same pattern and their differences are at an acceptable level. Similarly, in places where the amount of point clouds is at its maximum, the radar mode was also at maximum, and in cases where the amount of solar radiation energy was at a minimum, the same is valid for radar data.

To facilitate simulation and performance evaluation, an integrated workflow using the Grasshopper parametric modeling plugin for Rhinoceros3D software (Scott, 2010) and the Ladybug plugin (Roudsari, 2017) in Grasshopper was created for both solar radiation and building energy. This custom workflow integrates the functions of threedimensional parametric modeling of buildings, performance simulation, calculation of geometric variables and performance indicators, data processing, and visualization of results in an integrated manner. The meteorological data of Kermanshah, Iran, had been used in the form of an EPW file(EnergyPlus_Development_Team, 2020) as input for solar radiation and building energy simulation, which includes statistical data representing some important meteorological parameters for a particular place, such as global and hourly horizontal radiation, dry-bulb temperature (DBT), relative humidity, wind speed, etc.

The study of urban blocks in the intermediate level was done to identify urban blocks and the effects of different types and heights of the blocks of a neighborhood in absorbing the energy radiation of the surfaces. But as the figure below shows, examining all areas of a neighborhood comprising hundreds of blocks and thousands of plots with different height codes was a challenging and time-consuming task, which precludes further investigations by the user. Therefore, this study examined the average height of urban blocks in the sub-neighborhood scale. Thus, the Farhangian neighborhood, phase 1 of Kermanshah, was divided into four groups separated by the main routes. This ensures that the impact of the blocks on both sides of the street was minimized and did not interfere with the overall analysis.

To study and analyze solar radiation on a local scale, three-dimensional data in the form of mesh surfaces were required in addition to two-dimensional maps. This simulation did not just serve the purpose of a general data analysis but aimed to optimize the height of urban blocks and their research. Therefore, among the solar radiation analysis software, the Grasshopper plugin in Rhino software was used in this study to convert GIS maps to parametric 3D maps. The following algorithm was used in the Grasshopper environment to create these threedimensional patterns.

The final algorithm shown in Figure 10 consists of four parts prepared for optimization on microscale dimensions. This algorithm could optimize a set of urban blocks using changes in the height dimensions. In the first part of the algorithm, there were data recorders for storing data on changes in height and the amount of solar energy absorption. The second part placed the variables required for creating three-dimensional volume. The third part includes the variables needed to measure solar radiation. Finally, the tools needed to display the amount of radiation and the height dimension were placed on each block, which was an innovative measure taken by the researcher, with the possibility to delete or add other parts to it. The following sections examined radiation at the sub-neighborhood micro scale.

Based on the evaluation of the patterns in different seasons inFigure 11 and Figure 12, the facade surface data support our previous hypothesis that the energy absorption was constant compared to other surfaces in different directions. This means that energy absorption was consistently higher in the southward and westward surfaces of the region than in the northward and eastward ones. The amount of energy absorption in the height variable of 1 to 30 square meters, which was the range of height changes in our four groups, shows that climate change was almost the same for all elevated surfaces. The study of the higher height range was not performed due to the complexity and nature of the research that sought to optimize the status quo. On the other hand, ten residential floors equal to 30 meters were the allowed height limit in local regulations.

Examination of the amount of radiation in winter and summer in Figure 13 and Table 6 shows that solar radiation in summer was about 50% higher than solar radiation in winter. This ratio has always been constant in two-dimensional and three-dimensional studies. Changes can be seen in other seasons due to the angle of incidence, and other atmospheric factors such as clouds, which prevent radiation from reaching the ground.

The radiation and its nature in urban space were associated with many complexities due to many related variables and factors in the city. Assuming that the research time was constant, variables such as the height, length, and width of the block, the shape of the league, the number of lots and their orientation and Scale, etc., were just a few quantitative parameters affecting solar radiation in an urban block. Calculating solar radiation becomes more extended when the number of blocks increases and optimization becomes more complicated. Therefore, each of these algorithms had complex calculations and a specific type of method for obtaining the answer. For example, cold metal, genetic, ant colony, etc., are only part of the answer process. Given the continuous amount of radiation in a city, this study uses a genetic algorithm. This algorithm continuously examined different parts of the site. It optimized the amount of radiation based on the maximum amount of radiation with height changes in the range of 1 to 30 meters, taking into account changes in height dimension and other variables. This range and restriction in height dimension was the limited optimization time. Indeed, the higher the levels of these variables, the longer the optimization time.

The optimization data in Figure 14 show that the optimizer modifies its data over time and places a mark on the image by achieving new records. Figure 15 shows the optimized data, which was the final result of examining the height variable.

In Figure 15, the height dimension changes can be seen for both existing and optimized states. The height dimension changes had been increasing but did not reach the maximum level, i.e., 30 meters, remaining at the average range of 15 meters. According to the results, the difference between summer and winter shows different optimizations, as seen in the figure below. Height changes, in this case, needs further investigation, and no definitive conclusion can be drawn.

Examining more patterns in Figure 16 in terms of optimization in the subgroups is further revealing. The average height and the amount of optimization show that the data gradually move towards a more optimal pattern with changes in height variables. However, these patterns in all dimensions of change never moved to maximum height so that the average size is usually 50% of the general state. A careful look at the optimal state marked with gray showed that the amount of radiation absorbed in the urban context has increased by 5 to 10%.

The optimized sample in Table 7 andFigure 17 shows that the height optimization rate increased radiation energy absorption by only 3 to 5%. However, it should be noted that in this case, factors such as the amount of light absorption for indoor environments, reduced power consumption, and other unusual uses can have a significant effect on reducing energy consumption in large structures. On the other hand, even a one percent increase was energy absorption efficiency can dramatically reduce energy consumption because this one percent gives us usable energy. Therefore, it can be expected that only adjusting the height dimension in sub-neighborhoods relative to each other can significantly change the amount of solar energy absorption by 3 to 5 percent.

Height investigations, in this case, include hundreds of different data subdivided due to the nature of the research. However, studying these sub-neighborhoods requires a comprehensive investigation system that categorizes and analyzes the data. On the other hand, in this case, artificial intelligence was responsible for height optimization, and as Galapagos software used for optimization had fixed command codes to keep the optimization process and repeated this process up to 50 steps. It changed the height about 25 to 100 times for each neighborhood, depending on user settings, and re-apply radiation calculations for each step. If about 500 new record analyses did not obtain a new record of maximum radiation, the optimization was stopped. Therefore, the optimization process here was incomplete due to time constraints. However, previous researchers had pointed to this incomplete code in the optimization process and suggested a discrete approach obtained from the ant colony method for optimization processes where time was an essential element.

Figure 18 shows the height investigation in the optimized mode for two seasons of the year. These data are close to each other. We had set the time according to the season at this stage to limit time concerning speed so that the optimization speed can be increased in a shorter duration. It was emphasized that this height code was not the final optimal state, and the optimization process took more time.

Our preliminary studies in Figure 19 demonstrated the influence of radiation absorption from the Earth morphology on the macro-level data. This study shows that radiation absorption in different seasons with the proper location in an urban block can be expected to increase up to 200%, which was beyond the scope of this study. Finally, the final analysis of this study is to match the macro-level data and the average height of existing and optimized blocks. In the process of optimization based on genetic algorithm, it was expected that in optimization based on genetic algorithm, height changes occur based on maximum solar radiation in correspondence to changes of solar radiation in the macro-level data, i.e., satellite data. Data analyzed via adaptation of the optimized data and the height change data (Figure 19) shows that these data did not change according to the satellite algorithm and the morphological structure of the Earth. In other words, these changes were not significant, indicating a need for further research tailored to adaptive algorithms.

Based on analyses, simultaneous and intelligent use of satellite data in the initial development patterns and matching of height codes with such patterns can help determine the maximum radiation in the urban context. On the other hand, this study shows the need to develop intelligent algorithms based on machine learning algorithms and optimization algorithms in the future. The use of tools and algorithms on this basis can be helpful in the smart development of cities to receive maximum solar radiation and adapt the urban form to other climatic factors.

Although City and urban morphology is subject to numerous dependent and independent variables and reviewing and isolating variables in the research process is the only possible solution at this point, research in the field of energy, especially solar radiation, has always been a challenging topic. The need to produce early models of solar radiation levels for a city and ways to access them has been widely discussed. This study suggests a complete model in which the independent variable of average height and a dependent variable of solar radiation and optimization of the research variables were evaluated to achieve an optimal model in which the amount of solar radiation absorption is increased by about 3 to 5%.

The study of simulation at different scales has been assessed in macro and microdomains, each with its algorithm and type of analysis. However, in general, it can be concluded that in the macro-level data used in city planning whose documents and requirements are more related to land use, analyzes and simulations require higher speeds and less information. Thus, the process uses 2.5-dimensional DEM data, either collected through satellite data or point clouds, as fully described in the previous section. However, at the intermediate level, especially at the microlevel, 3D detailed data is needed, which is done using simulation within longer durations with higher accuracy. The only difference between the micro and the intermediate level is the amounts of variables being measured. At the intermediate level, the number of data should be controlled because the amount of data, in this case, is very high, and there may be an increase in computational error. However, in the micro approach, the independent data is lower, and the analysis speed is naturally higher.

The main purpose of this study was to provide an optimal model to evaluate the maximum amount of solar energy absorption at urban block levels. First, the morphological variables at the urban block level were studied. Then solar radiation energy at the macro Scale and micro scales were investigated. Using two radar and point cloud techniques at the macro Scale, solar radiation energy was calculated in the study area, which indicated the importance of macro data analysis at the neighborhood scale. And even urban blocks in the field of planning.

On the other hand, the results showed that radiation data in different seasons produce fixed spatial data compared to each other. The method of calculating radiation time, a significant variable, was also calculated. Then in the next part, the neighborhood was divided into four sub-neighborhoods to measure the impact of different blocks on each other. In this part, optimization was done based on height dimension, indicating that the limitations in optimizer algorithms do not allow the measurement of more variables due to a large amount of information and data. Results on the average height at urban block-level showed an increase of 3 to 5%.

On the other hand, a comprehensive algorithm was presented, starting a new optimization and architectural algorithms approach, which researchers and relevant laboratories can discuss. Adaptation of genetic algorithms to satellite data and spatial variables can help solve significant problems in solar radiation calculation. The first mode shifts the radiation and energy data to point-to-point spatial data, expanding the data beyond the general method for different cities. It opens a new window into artificial intelligence algorithms for a better understanding of the structure of the Earth and its impact on cities.