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A feature fusion technique for improved non-intrusive load monitoring

机译：一种改进非侵入式负荷监测的特征融合技术

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Load identification is an essential step in Non-Intrusive Load Monitoring (NILM), a process of estimating the power consumption of individual appliances using only whole-house aggregate consumption. Such estimates can help consumers and utility companies improve load management and save power. Current state-of-the-art methods for load identification generally use either steady state or transient features for load identification. We hypothesize that these are complementary features and so a hybrid combination of them will result in an improved appliance signature. We propose a novel hybrid combination that has the advantage of being low-dimensional and can thus be easily integrated with existing classification models to improve load identification. Our improved hybrid features are then used for building appliance identification models using Naive Bayes, KNN, Decision Tree and Random Forest classifiers. The proposed NILM methodology is evaluated for robustness in changing environments. An automated data collection setup is established to capture 7 home appliances aggregate data under varying voltages. Experimental results show that our proposed feature fusion based algorithms are more robust and outperform steady state and transient feature-based algorithms by at least +9% and +15% respectively.
机译：负载识别是非侵入式负载监测（NILM）的基本步骤，仅使用全室骨料消耗估计各个设备的功耗的过程。这种估计值可以帮助消费者和公用事业公司提高负载管理和节省权力。用于负载识别的当前最先进的方法通常使用稳态或瞬态特征进行负载识别。我们假设这些是互补特征，因此它们的混合组合将导致改进的设备签名。我们提出了一种新的混合组合，其具有低维度的优点，因此可以容易地与现有的分类模型集成以改善负载识别。然后，我们改进的混合功能用于使用Naive Bayes，Knn，决策树和随机林分类器建立设备识别模型。评估所提出的尼尔方法论在改变环境中的鲁棒性。建立自动数据收集设置以捕获7个家庭设备在不同电压下的聚合数据。实验结果表明，我们所提出的特征融合基于稳定状态和基于瞬态特征的算法分别为至少+ 9％和+ 15％。

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