Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Developing machine-learning meta-models for high-rise residential district cooling in hot and humid climate
Cooling accounts for a significant amount of energy consumption in hot and humid climates, and district cooling is an energy-efficient solution. During its planning stage, an accurate and fast prediction of district cooling loads is required to assist decision-making. This study investigated the development of machine-learning-based meta-models to predict the cooling load of high-rise residential buildings at a district scale. Four machine-learning models have been evaluated, including Multiple Linear Regression, Support Vector Regression, Artificial Neural Networks (ANN), and eXtreme Gradient Boosting. The meta-model development starts with the sensitivity analysis to determine the critical parameters as independent variables. The results show that the ANN model with 30 neurons of the hidden layer, trained by eight epochs with 20% sample data, manifested superior performance when predicting monthly energy use intensity for the testing cases. A minimum of eight independent variables for the meta-model was also shown to be acceptable.
Developing machine-learning meta-models for high-rise residential district cooling in hot and humid climate
Cooling accounts for a significant amount of energy consumption in hot and humid climates, and district cooling is an energy-efficient solution. During its planning stage, an accurate and fast prediction of district cooling loads is required to assist decision-making. This study investigated the development of machine-learning-based meta-models to predict the cooling load of high-rise residential buildings at a district scale. Four machine-learning models have been evaluated, including Multiple Linear Regression, Support Vector Regression, Artificial Neural Networks (ANN), and eXtreme Gradient Boosting. The meta-model development starts with the sensitivity analysis to determine the critical parameters as independent variables. The results show that the ANN model with 30 neurons of the hidden layer, trained by eight epochs with 20% sample data, manifested superior performance when predicting monthly energy use intensity for the testing cases. A minimum of eight independent variables for the meta-model was also shown to be acceptable.
Developing machine-learning meta-models for high-rise residential district cooling in hot and humid climate
Jia, B. (Autor:in) / Hou, D. (Autor:in) / Kamal, A. (Autor:in) / Hassan, I. G. (Autor:in) / Wang, L. (Autor:in)
Journal of Building Performance Simulation ; 15 ; 553-573
04.07.2022
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Application of District Heating and Cooling in High-Rise Residential District
| British Library Conference Proceedings | 1997
| DOAJ | 2020
High-Rise Residential District Morphology Optimization for Enhancing the Green Space Cooling Effect
| DOAJ | 2024
High-rise residential district and construction method thereof
| Europäisches Patentamt | 2020
Green Building Clusters for Residential Developments: Developing District Cooling Systems
| Springer Verlag | 2021