Benchmarking Energy Use in Schools
Benchmarking Energy Use in Schools
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学校能源使用基准测试
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
R. Sharp
中科院分区：
文献类型：
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作者：
Terv;R. Sharp
1992年，美国各地政府在公立学校的能源上花费了大约50亿美元，平均每个州1亿美元。这意味着教育资金的巨大流失，其中一部分（通过建设系统和提高运营效率获得）可以用于更重要的教育需求。州和地方政府知道存在相当大的机会，但如何以及从哪里开始面临挑战。找出最有潜力、最容易、成本最低的能源表现最差的企业，是激励地方政府采取行动的关键。能源基准是实现这一目的的一个很好的工具。1992年美国能源情报署的商业建筑能耗调查（CBECS）数据库的调查作为能源基准的地方政府拥有的学校。从CBECS得出的平均能源使用值被证明是穷人的能源基准。然而，CBECS得出的建筑物能源使用值的简单分布被证明是当地学校可靠的能源基准。这些可以用来衡量当地公立学校的能源绩效。使用逐步，线性回归分析，电力使用在当地学校的主要决定因素被发现是总建筑面积，建筑年份，使用步入式冷却器，电冷却，非电能使用，屋顶建筑，暖通空调的操作责任。决定因素因学校位置而异。虽然基于简单分布的基准测试是一种很好的方法，但详细介绍了一种改进的基准测试方法，该方法可以解释这些额外的能源使用驱动因素。
Local governments across the United States spent approximately $5 billion, an average of $100 million per state, on energy for their public schools in 1992. This represents a tremendous drain on education dollars of which part (captured through building system and operational efficiency improvements) could be directed toward more important educational needs. States and local governments know there are sizeable opportunities, but are challenged by how and where to start. IdentifLing the worst energy performers, with the most potential, easily and at low cost is a key in motivating local governments into action. Energy benchmarking is an excellent tool for this purpose. The 1992 US Energy Information Administration’s Commercial Buildings Energy Consumption Survey (CBECS) database is investigated as a source for energy benchmarks for local-government-owned schools. Average energy use values derived from CBECS are shown to be poor energy benchmarks. Simple distributions of building energy use values derived from CBECS, however, are shown to be reliable energy benchmarks for local schools. These can be used to gauge the energy performance of your local public school. Using a stepwise, linear-regression analysis, the primary determinants of electric use in local schools were found to be gross floor area, year of construction, use of walk-in coolers, electric cooling, non-electric energy use, roof construction, and HVAC operational responsibility. The determinants vary depending on the school’s location. While benchmarking based on simple distributions is a good method, an improved benchmarking method which can account for these additional drivers of energy use is detailed.