City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique
City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique
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DOI：
10.1016/j.buildenv.2020.106667
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发表时间：
2020-04-01
影响因子：
7.4
通讯作者：
Cho, In Ho
中科院分区：
文献类型：
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作者：
Jahani, Elham;Cetin, Kristen;Cho, In Ho
城市地区能源消费的增长增加了规划未来能源系统的重要性。因此，提高城市规模能源消费预测的建模能力至关重要。在这项研究中，遗传算法为基础的数值矩匹配（GA-NMM）方法被采用作为一个主要的不确定性估计技术，预测一个大的数据集的单户住宅的用电量，利用能源审计和评估数据的关键功能。该数据用作GA-NMM的输入，以开发一组表示数据集统计特征的指数建筑物和相关加权因子。然后，使用EnergyPlus中基于物理的能源建模为索引建筑物开发能源模型。这些，结合起来，被用来估计所研究的数据集的单户住宅的能源行为。所提出的方法被应用到一个大的数据集的雪松福尔斯，爱荷华州，预计每年和每月的电力消耗模型计算和测量数据进行比较。Cedar福尔斯单户建筑物的预计现场用电量估计为10，219千瓦时/年，在测得的平均年用电量的6%以内。在月水平上，均方根误差和平均偏差误差的变异系数分别为7.8%和4.5%。该方法可用于生成小的代表性住宅集合，以展示较大的住宅集合的能量行为。
Growing energy consumption in urban areas has increased the importance of planning for future energy systems. Thus, improving the modeling abilities for predicting energy consumption at the city scale is critical. In this study, a Genetic Algorithm-Based Numerical Moment Matching (GA-NMM) method is adopted as a primary uncertainty estimation technique to predict the electricity consumption of a large dataset of single family homes by utilizing key features in energy audit and assessors data. This data is used as an input to the GA-NMM to develop a set of index buildings and associated weighting factors that represent statistical characteristics of the dataset. Energy models are then developed for the index buildings using physics-based energy modeling in EnergyPlus. These, in combination, are used to estimate the energy behavior of single family homes of the studied dataset. The proposed method is applied to a large dataset of 8370 single family homes in Cedar Falls, Iowa, where the expected annual and monthly electricity consumption from the model is calculated and compared with measured data. The expected site electricity consumption for single family buildings in Cedar Falls is estimated as 10,219 kWh/yr, which is within 6% of the measured average annual electricity consumption. At a monthly level, the Coefficient of Variation of Root Mean Square Error and Mean Bias Error are 7.8% and 4.5%, respectively. This method can be used to generate small set of representative homes for demonstrating the energy behavior of a larger set of homes.