496 research outputs found

    Exploring the Applicability of Building Energy Performance Certification Systems in Underground Stations in China

    Get PDF
    To improve the energy efficiency of underground metro stations, and in view of the absence of a comprehensive energy performance evaluation system for underground stations, this study introduced building Energy Performance Certification (EPC) tools into underground stations and conducted a comparative analysis of their applicability. The findings indicated that due to the unique characteristics of underground stations, China’s current EPC system was inapplicable to them. Specifically, (1) for basic items, although evaluation methods were available, due to the limited energy use data for the statistical method, the self-reference method was preferred, but its calculation encountered issues with missing reference values; (2) for prescribed items, the emphasis should be placed on the energy efficiency requirements of energy use systems rather than those of the thermal performance of envelopes; (3) for alternative items, the energy recovery measures related to the heat dissipation of trains and the piston wind should be addressed. Furthermore, a case study was conducted for verification of the proposed energy evaluation method, and the EPC system was updated based on the results of the comparison. The authors hope that this study will help improve China’s energy evaluation methods for underground stations and serve as a reference for expanding the EPC system to include public transportation buildings

    Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

    Full text link
    Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermediate rationales for solving a problem, by providing a series of reasoning steps in the demonstrations. Despite its success, there is still little understanding of what makes CoT prompting effective and which aspects of the demonstrated reasoning steps contribute to its performance. In this paper, we show that CoT reasoning is possible even with invalid demonstrations - prompting with invalid reasoning steps can achieve over 80-90% of the performance obtained using CoT under various metrics, while still generating coherent lines of reasoning during inference. Further experiments show that other aspects of the rationales, such as being relevant to the query and correctly ordering the reasoning steps, are much more important for effective CoT reasoning. Overall, these findings both deepen our understanding of CoT prompting, and open up new questions regarding LLMs' capability to learn to reason in context.Comment: ACL-23 Camera Ready. Code and model input/output are available at https://github.com/sunlab-osu/Understanding-Co
    corecore