Predicting solar radiation in the urban area: A data-driven analysis for sustainable city planning using artificial neural networking

Predicting solar radiation in cities using the Artificial Neural Network model (ANN) is a pioneering step in transforming future-oriented city planning using solar energy. This research harnesses vast datasets to forecast the average annual solar radiation, considering minimal urban information across various urban attributes, including coordinates (X, Y, Z), average height, inhabited and non-occupied areas, and the Azimuth angle. Our method employed parametric design and remote sensing to generate this dataset and then used the ANN model to make predictions and simulations. Urban attributes of 20 cities were examined, including Casablanca, Abu Dhabi, Cape Town, Dublin, Havana, Melbourne, Rome, Singapore, Nairobi, Mumbai, New York, Nagoya, Sao Paulo, Tehran, Madrid, Toronto, Antananarivo, Beijing, Lisbon, and Paris. This data-driven approach trains our ANN model to discern complex and nonlinear relationships between independent and dependent variables and thus enables our model to predict solar radiation in urban cities. Our data training results indicate that the output (the minimum solar radiation each year of the cities) can be predicted using the study input variables with a loss of 0.01, a mean squared error of 0.01, and an R2-squared value of 85%. Such predictions can refine urban designs of buildings, public spaces, and various urban infrastructures to optimize solar energy use, reducing environmental impacts and fossil fuel reliance, thus aiding climate change mitigation and sustainability. Our findings underscore the integral association between solar radiation and sustainable urban evolution, giving urban planners and researchers sustainable strategies for advancing energy efficiency and ecological equilibrium.