Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups
Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups
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DOI：
10.1016/j.enbuild.2017.09.060
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发表时间：
2017-12
影响因子：
6.7
通讯作者：
K. Song;Nahyun Kwon;Kyle Anderson;Moonseo Park;Hyun-soo Lee;SangHyun Lee
中科院分区：
文献类型：
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作者：
K. Song;Nahyun Kwon;Kyle Anderson;Moonseo Park;Hyun-soo Lee;SangHyun Lee
准确的能耗预测对于优化建筑能耗性能至关重要。迄今为止，已经进行了大量的努力来提高预测准确性，特别是在关注建筑物中的占用者的存在时。不幸的是，在使用占用数据预测建筑物能耗时，仍然存在两个重大障碍。首先，在模型开发过程中很少考虑最终用户群体之间的占用多样性。其次，由于居住者行为的差异，居住与能源消耗的相关性可能很弱。因此，本研究的目的是调查如何占用相关的最终用户群体的特征影响预测性能。为了实现这一目标，构建了基于数据挖掘的预测模型来模拟建筑热行为。实验结果表明，该预测模型在考虑不同的占用率及其与能源使用的相关性时，预测精度得到了提高。此外，仅使用最少量的历史数据就实现了显著的预测准确性。利用所提出的预测模型，可以获得关于能量使用模式的更详细的信息（例如，负载形状、能源使用量）。因此，设施管理人员将能够根据最终用户群个性化能耗设备的操作，以减少能耗，而不会影响居住者的热舒适度。
Accurate predictions of energy consumption are essential to optimizing building energy use performance. To date, substantial efforts have been undertaken to improve prediction accuracy, specifically while focusing on occupants’ presence in buildings. Unfortunately, two significant obstacles remain when predicting building energy consumption using occupancy data. First, occupancy diversity among end-user groups is rarely considered during model development. Second, occupancy’s correlation with energy consumption may be weak due to variances in occupant behavior. Therefore, this research aims to investigate how occupancy-related characteristics of end-user groups affect prediction performance. In order to achieve this objective, a data mining-based prediction model is constructed to mimic building thermal behaviors. The experimental results using the proposed prediction model make it evident that prediction accuracy is improved when considering diverse occupancy and its correlation with energy use. In addition, significant prediction accuracy is achieved using only a minimal amount of historical data. With the proposed prediction model, it is possible to obtain more detailed information about energy use patterns (e.g., load shape, the amount of energy use) for end-user groups. Thus, facility managers will be able to personalize the operation of energy-consuming equipment depending on end-user group for reducing energy consumption without compromising occupants’ thermal comfort.