Reporting Interval Impact on Deep Residential Energy Measurement Prediction

G. Stamatescu, I. Ciornei, Radu Plamanescu, A. Dumitrescu, M. Albu
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引用次数: 3

Abstract

Forecasting and anomaly detection for energy time series is emerging as an important application area for computational intelligence and learning algorithms. The training of robust data-driven models relies on large measurement datasets sampled at ever increasing rates. Thus, they demand large computational and storage resources for off-line power quality analysis and for on-line control in energy management schemes. We analyze the impact of the reporting interval of energy measurements on deep learning based forecasting models in a residential scenario. The work is also motivated by the development of embedded energy gateways for online inference and anomaly detection that avoid the dependence on costly, high-latency, cloud systems for data storage and algorithm evaluation. This, in turn, requires increased local computation and memory requirements to generate predictions within the control sampling period. We report quantitative forecasting metrics to establish an empirical trade-off between reporting interval and model accuracy. Additional results consider the time scale variable feature extraction using a time series data mining algorithm for multi-scale analytics.
报告间隔对深层住宅能源计量预测的影响
能量时间序列的预测和异常检测是计算智能和学习算法的重要应用领域。健壮的数据驱动模型的训练依赖于以不断增加的速率采样的大型测量数据集。因此,它们需要大量的计算和存储资源来进行离线电能质量分析和能源管理方案中的在线控制。我们分析了住宅场景中能源测量报告间隔对基于深度学习的预测模型的影响。这项工作还受到用于在线推理和异常检测的嵌入式能源网关的开发的推动,这些网关避免了对昂贵、高延迟的云系统的依赖,用于数据存储和算法评估。这反过来又需要增加本地计算和内存需求,以便在控制采样周期内生成预测。我们报告定量预测指标,以建立报告间隔和模型准确性之间的经验权衡。其他结果考虑了使用时间序列数据挖掘算法进行多尺度分析的时间尺度变量特征提取。
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