Calculating, optimizing, and predicting solar radiation in different types of urban blocks
Description
This dataset accompanies the research article "Using intelligent multi-objective optimization and artificial neural networking to achieve maximum solar radiation with minimum volume in the Archetype urban block". It contains multi-dimensional data gathered and processed during the study of urban block design optimization. The goal was to maximize solar radiation exposure while minimizing the volume of the urban blocks, contributing to efficient and sustainable urban planning. Data was collected through simulations that employed intelligent multi-objective optimization techniques and artificial neural network modeling. Genetic algorithm optimization was utilized to generate a Pareto front, aiming to find an optimal balance between solar radiation maximization and volume minimization. Data Description: The dataset includes the following fields: Scale: The scale factor applied to the urban block model. Orientation: The orientation angle of the urban block in degrees. Average_Height: The average height of the urban structures within the block. Total Radiation: The calculated total solar radiation received by the block. Volume: The total volume of the urban block. Area: The surface area of the urban block. Type: The categorical type of the urban block. Num. of blocks: The number of discrete blocks within the urban model. Usage Notes: Researchers and urban planners can use this dataset to understand the implications of urban block design on solar radiation exposure and volume usage. The data should be interpreted in the context of the associated research article. The dataset is suitable for further analysis with machine learning models, particularly for training and validating artificial neural networks. Quality Assurance: Each data entry has been verified for consistency and accuracy with respect to the simulation outputs from the optimization algorithms. Associated Publications: Veisi, O., Shakibamanesh, A., & Rahbar, M. (2022). Using intelligent multi-objective optimization and artificial neural networking to achieve maximum solar radiation with minimum volume in the archetype urban block. Sustainable Cities and Society, 86, 104101.https://doi.org/10.1016/j.scs.2022.104101 Data Accessibility: The dataset is available for public access and use, with appropriate citation of the associated research article.