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Forecasting the Electricity Consumption in a Higher Education Institution

机译:预测大学的用电量

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The objective of this paper is to present a mathematical representation by Regression Analysis that enables the projection of electricity consumption according to the built area and population in Higher Education Institutions (HEI) and to define an Indicator that contemplates the most significant variable in consumption. of electric power. The Null Hypothesis -H0 is that in a HEI the most appropriate indicator is the Kilo Watt Hour per square meter (kWh / m2) as proposed by the Ministry of Planning and Management - MP. The research universe is 2,368 HEI, identified in a report from the Ministry of Education (2015). As Sample and case study, data from the thirteen Campi of the Federal Technological University of Paraná (UTFPR) are used. As a computational tool we use the IBM SPSS Statistics Base Software for Windows version 23 from SPSS Inc .. For the considered Sample and research design, the conclusion is that the null hypothesis is rejected accepting that the most significant indicator is the kilo. Watt Time per user (kWh / user). This conclusion does not exclude the relationship between constructed area and Energy Consumption, but reveals that it is not as significant as the number of individuals in HEI for this sample.
机译:本文的目的是通过回归分析提出一种数学表示法,该方法能够根据高等学校(HEI)的建筑面积和人口来预测用电量,并定义一个考虑耗电量最大变量的指标。电力。零假设-H0是在HEI中最合适的指标是规划和管理部(MP)提出的每平方米千瓦时(kWh / m2)。根据教育部的一份报告(2015年),研究领域为2,368个HEI。作为样本和案例研究,使用了来自巴拉那联邦技术大学(UTFPR)的十三坎皮的数据。作为一种计算工具,我们使用SPSS Inc.的Windows版本23的IBM SPSS Statistics Base Software。对于经过考虑的示例和研究设计,结论是,接受最重要的指标是千克,则拒绝零假设。每位使用者的瓦特时间(千瓦时/位使用者)。该结论不排除建筑面积与能源消耗之间的关系,但表明该样本不像HEI中的个体数量那么重要。

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