Document Type : Research Paper
Authors
1 Assistant Prof, of Architecture, Department of Architecture and Urban Design, Iran University of Science and Technology, Tehran, Iran.
2 Associate Prof. of ,Architecture. Department of Art and Architecture, Tarbiat Modares University, Tehran, Iran.
3 Prof. of Architecture,Department of Art and Architecture, Tarbiat Modares University, Tehran, Iran.
4 Professor, , School of Mechanic Engineering, Iran University of Science & Technology
Abstract
This research is to study the capabilities of artificial neural network (ANN) for predicting solar radiance in an urban context in order to materialize the concept of high-performance architecture. Literature review of the research implies that artificial intelligence (AI) is going to be a new emerging tool to contribute to high-performance architecture, as well as a way of thinking for a significant paradigm shift in environmental sustainability.
In the first step, a rule-based method is applied to generate the dataset. In the next step, three different ANN models with different architectures are defined. These models are trained with the generated dataset and regarding the defined algorithm architecture, different results are predicted.
The results indicate the precision of each model in predicting the amount of received solar radiation in a new sample location. Finally, these results are compared and the best ANN architecture is selected. The proposed model in this research could be generalized to other similar simulations and it has two main applications. First, it could predict the target parameter instantly without intensive computation. Secondly, it could fit a function for simulating a sustainable parameter only with the given input and output dataset and without needing to know any specific rules for the simulation.
The results conclude that AI might be introduced as a comprehensive methodology for sustainable design in contemporary architecture. However, the research shows capability of ANN for outlining and predicting environmental sustainable parameters particularly.
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