Comparing four machine learning algorithms for household non-intrusive load monitoring

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-06-24 DOI:10.1016/j.egyai.2024.100384
Thomas Lee Young, James Gopsill, Maria Valero, Sindre Eikevåg, Ben Hicks
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Abstract

The combination of Machine Learning (ML), smart energy meters, and availability of household appliance energy profile data has opened new opportunities for Non-Intrusive Load Monitoring (NILM). However, the number of options makes it challenging in selecting optimal combinations for different energy applications, which requires studies to examine their trade-offs.

This paper contributes one such study that investigated four established ML approaches – K Nearest Neighbour (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) – and their performance in classifying appliance events from Alternating Current (AC) and Root Mean Square (RMS) energy data where the sampling frequency and training dataset set size was varied (10 Hz–1 kHz and 50–2000 examples per class, respectively). The computational expense during training, testing and storage was also assessed and evaluated with reference to real-world applications.

The CNN classifier trained on AC data at 500 Hz and 11,000 examples gave the best F1-score 0.989 followed by the KNN classifier 0.940. The storage size required by the CNN models was 3̃MB, which is very close to fitting on cost-effective embedded system microcontrollers. This would prevent high-rate data needing to be sent to the cloud as analysis could be performed on edge computing Internet-of-Things (IoT) devices.

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比较用于家庭非侵入式负荷监测的四种机器学习算法
机器学习(ML)、智能能源计量表和家用电器能源概况数据的结合为非侵入式负荷监测(NILM)带来了新的机遇。然而,由于选项众多,要为不同的能源应用选择最佳组合极具挑战性,这就需要对其权衡利弊进行研究。本文就是这样一项研究,研究了四种成熟的 ML 方法--K 最近邻 (KNN)、支持向量机 (SVM)、极梯度提升 (XGBoost) 和卷积神经网络 (CNN)--以及它们在对交流 (AC) 和均方根 (RMS) 能源数据中的家电事件进行分类时的性能,其中采样频率和训练数据集大小各不相同(分别为 10 Hz-1 kHz 和每类 50-2000 个实例)。在实际应用中,还对训练、测试和存储过程中的计算费用进行了评估和评价。在 500 Hz 和 11,000 个示例的交流电数据上训练的 CNN 分类器给出了最佳 F1 分数 0.989,其次是 KNN 分类器的 0.940。CNN 模型所需的存储容量为 3̃MB,非常接近成本效益型嵌入式系统微控制器的容量。这可以避免将高速数据发送到云端,因为分析可以在边缘计算的物联网(IoT)设备上进行。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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