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Estimation models for heating energy and electricity costs

机译：供热和电力成本估算模型

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摘要

Heating energy and electricity represent a significant proportion of the overall financial expenditures over the life cycle of buildings and play a decisive role in the assessment of ecological quality. To ensure ecological and economical sustainability, the planning and construction of energy efficient buildings have become increasingly important. The present study introduces statistical models for the estimation of energy costs during the planning process as well as for the purpose of monitoring and controlling costs during operation including a wide range of facility types. Based on a data sample of 206 occupied facilities located in Germany, regression models are presented in detail including significant predictor variables. The provision of correctly specified models as well as a consistent and unbiased estimation beyond the employed data is verified by validation tests. The practical significance of the determined variables is presented by the calculation of various scenarios. The best estimation accuracy is indicated for regression models with transformed response and predictor variables achieving mean absolute percentage errors of 20.5% (heating energy) and 20.7% (electricity), respectively. An extension of the data sample, a consideration of further facility types, and an implementation of additional statistical methods may be considered by future approaches to improve the estimation accuracy.
机译：在建筑物的整个生命周期中，热能和电力占整个财政支出的很大一部分，并且在生态质量评估中起着决定性的作用。为了确保生态和经济的可持续性，节能建筑的规划和建设变得越来越重要。本研究介绍了统计模型，用于在规划过程中估算能源成本，以及用于监视和控制运行期间的成本，包括各种设施类型。基于位于德国的206个被占用设施的数据样本，详细介绍了回归模型，其中包括重要的预测变量。验证测试可以验证提供正确指定的模型以及所采用数据以外的一致且无偏的估计。确定的变量的实际意义通过各种方案的计算来表示。对于具有转换后的响应和预测变量的回归模型，表明最佳的估计精度是平均绝对百分比误差分别为20.5％（加热能量）和20.7％（电）。未来的方法可能会考虑数据样本的扩展，其他设施类型的考虑以及其他统计方法的实现，以提高估计准确性。

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