[Full Picture] Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms-所有数据库

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Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms-所有数据库

Source: webvpn.ujs.edu.cn

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Summary Analysis Research

Article summary:

1. This study proposes a data-driven workflow for comprehensive performance assessment and rapid prediction of office buildings.

2. The method was applied to an office building in the hot summer and cold winter regions of China, achieving a precision of 0.77, recall of 0.59, and F-1 score of 0.75 for categorical prediction by the XGBoost algorithm.

3. The method facilitates the optimization potential of integrated solar and thermal performances in the early design phase of office buildings while significantly improving the efficiency of interaction and feedback between design decisions and their performance evaluation.

Article analysis:

The article is generally reliable as it provides detailed information on its research methodology, results, and conclusions. It also cites relevant sources to support its claims, such as National Natural Science Foundation of China (NSFC)52178017 and Opening Fund of Key Laboratory of Inter-active Media Design Equipment Service Innovation, Ministry of Culture and Tourism20204. Furthermore, it is written in an objective manner without any promotional content or partiality towards any particular point of view or opinion.

However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, the article does not provide any evidence for its claims regarding the efficacy of genetic optimization algorithms in improving indoor/outdoor thermal performances or how this method can improve efficiency in terms of interaction/feedback between design decisions and their performance evaluation. Additionally, there is no discussion on possible risks associated with using these algorithms or any counterarguments that could be explored further to gain a better understanding on this topic.

Topics for further research:

Risks associated with genetic optimization algorithms Interaction/feedback between design decisions and performance evaluation Efficacy of genetic optimization algorithms in improving indoor/outdoor thermal performances Counterarguments to genetic optimization algorithms Advantages of using genetic optimization algorithms Limitations of genetic optimization algorithms