%0 Journal Article
%T 基于K-Means聚类算法的公共机构能耗定额指标模型分析及应用&#8212;&#8212;以广西某市为例<br>Analysis and Application of Energy Consumption Quota Indicator Model for Public Institutions Based on K-Means Clustering Algorithm&#8212;A Case Study of a City in Guangxi
%A 卢俊宇
%A 杨天山
%A 袁功林
%J Sustainable Development
%P 32-40
%@ 2160-7559
%D 2025
%I Hans Publishing
%R 10.12677/sd.2025.156158
%X 公共机构能耗定额指标是用于衡量和规范公共机构能源消耗的标准&#65292;通过指导和规范公共机构的能源使用&#65292;提高能源利用效率并降低能源消耗强度。编制公共机构能耗定额标准应遵循科学合理原则&#65292;确保公共机构正常运行的需要&#65292;同时促进节能发展。相比传统的统计方法&#65292;机器学习方法更具灵活性。本文通过机器学习算法中的K-means聚类算法制定能耗定额指标值&#65292;构建适用公共机构的能耗定额计算模型。并利用该计算模型针对广西某市三类公共机构的建筑能耗制定出相关的能耗定额指标值。最后对广西公共机构的节能工作提出相关建议&#65292;以期建立长效机制&#65292;建设节能型、绿色发展型公共机构。<br>Public institution energy consumption quota indicators are standards used to measure and regulate the energy consumption of public institutions. By guiding and standardizing the energy use of public institutions, these indicators aim to improve energy efficiency and reduce energy consumption intensity. The formulation of energy consumption quota standards for public institutions should adhere to the principle of scientific rationality, ensuring the normal operational needs of public institutions while promoting energy-saving development. Compared to traditional statistical methods, machine learning methods offer greater flexibility. This paper employs the K-means clustering algorithm from machine learning to establish energy consumption quota values and constructs a calculation model of energy consumption quota suitable for public institutions. Using this model, the paper develops relevant energy consumption quota values for the building energy consumption of three types of public institutions in a certain city in Guangxi. Finally, the paper offers suggestions for the energy-saving work of public institutions in Guangxi, with the aim of establishing a long-term mechanism and constructing energy-saving and green development-oriented public institutions.
%K 公共机构&#65292
%K 能耗定额&#65292
%K 聚类分析<br>Public Institutions
%K Energy Consumption Quota
%K Clustering Analysis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=117076