RT info:eu-repo/semantics/article
T1 Forecasting Performance Indicators of a Single-Channel Solar Chimney Using Artificial Neural Networks
A1 Torres-Aguilar, Carlos
A1 Moreno, Pedro
A1 Rossit, Diego
A1 Nesmachnow, Sergio
A1 Aguilar-Castro, Karla M.
A1 Macias-Melo, Edgar V.
A1 Hernández-Callejo, Luis
AB Solar chimneys are renewable energy systems designed to enhance natural ventilation, improvingthermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, glass thickness, paint, wall material, measurement date, and orientation. The case study examines the four cardinal orientations and weather data from Mexico City, covering the period from 01 January to 31 December 2024. The main results indicate that the proposed artificial neural network models achieved higher coefficient of determination values (0.905-0.990) than the baseline method across performance indicators of the solar chimney system, demonstrating greater accuracy and improved generalization. The proposed approach highlightsthe potential of using artificial neural networks as a decision-making tool in the design stage of solar chimneys in sustainable architecture.
PB Tech Science Press
SN 15261492
YR 2025
FD 2025-11-27
LK https://uvadoc.uva.es/handle/10324/80151
UL https://uvadoc.uva.es/handle/10324/80151
LA spa
NO Forecasting Performance Indicators of a Single-Channel Solar Chimney Using Artificial Neural Networks. CMES - Computer Modeling in Engineering and Sciences , november, 2025, p. 1-23
DS UVaDOC
RD 28-nov-2025