Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

Zhen, Jiang; Shi, Di; Guo, Xijuan; Xu, Guangyue; Yu, Li; Jing, Chaoyang

doi:10.3390/en11061401

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…Furthermore, the time granularity of electricity consumption records, especially generated by different brands of smart meters, often exhibits significant variation [28,29]. For example, occasional instances of missing records may occur, leading to situations where only one or two power consumption records are available per 15 min, despite the intended frequency of consumption measurement being at the minute level [30].…”

Section: Related Work 21 Challenges In the Smart Meter Big Data Analy...mentioning

confidence: 99%

“…Consequently, within a specific time interval, such as 15 min, the quantity of accurately recorded electricity usage records can fluctuate [30]. The literature often discusses the sampling frequency of smart meters, which can vary significantly, ranging from as fast as thousands of samples per second (expressed in kHz) to as long as two hours [28,29]. Instant electricity consumption data collected over a few minutes are usually sufficient for most analyses, aiding residents in monitoring and conserving electricity usage.…”

Section: Related Work 21 Challenges In the Smart Meter Big Data Analy...mentioning

confidence: 99%

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Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

Wang,

Ho,

Chiang

et al. 2023

Energies

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In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency.

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Section: Related Work 21 Challenges In the Smart Meter Big Data Analy...mentioning

confidence: 99%

Section: Related Work 21 Challenges In the Smart Meter Big Data Analy...mentioning

confidence: 99%

Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

Wang,

Ho,

Chiang

et al. 2023

Energies

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“…Although it is possible to achieve finegrained data with reliable communications through advanced smart metering technologies, there still exist missing values in databases. Such missing values occur due to unexpected device power off, communication failure, measuring error, or other unknown reasons [11], [12]. Therefore, missing values should be properly recovered during the preprocessing stage.…”

Section: Introductionmentioning

confidence: 99%

Denoising Autoencoder-Based Missing Value Imputation for Smart Meters

Ryu

Kim

2020

IEEE Access

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Electric load data are essential for data-driven approaches (including deep learning) in smart grid, and advanced smart meter technologies provide fine-grained data with reliable communications. Despite the recent development of smart metering devices, however, missing data still arise due to unexpected device power off, communication failure, measuring error, or other unknown reasons. In this paper, we investigate a deep learning framework for missing imputation of smart meter data by leveraging a denoising autoencoder (DAE). Then, we compare the performance of the proposed DAE with traditional methods as well as other recently developed generative models, e.g., variational autoencoder and Wasserstein autoencoder. The proposed DAE based imputation shows significantly better results compared to other methods in terms of root mean square error (RMSE) by up to 28.9% for point-wise error, and by up to 56% for daily-accumulated error. INDEX TERMS Deep learning, smart grid, missing imputation, smart meters, denoising autoencoder, generative model, daily load profile (DLP).

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Fusion of energy sensors with missing values

et al. 2023

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In Smart Energy Grids, the information flow used to make decisions is the result of fusion of different sources. Communication latency, possible sensor faults and inaccuracies, may negatively impact the data quality and hence the taken decisions. For these reasons, the construction of a robust representation of the input signals that replaces and/or corrects the inaccurate data is crucial for effective classification, anomaly detection and planning. Recent works on Data Fusion and data imputation suggest that the usage of other signals in the same context can empower the representation and can be a useful preprocessing task. In this work we describe an Autoencoder-based data fusion architecture with convolutional layers, skip connections and ad-hoc augmented training sets for data imputation applied to the power consumption measurements obtained by different sub-meters. Among the investigated architectures, the approach with the shared convolutional layers and an augmented dataset that consider missing data in the random positions and located in the central part (AE-A-ALL-CNN), is the most promising one. In presence of one half of the input signal, in the central part, completely erased, it improves the imputation capability, respect to two most employed approaches (denoising autoencoder and MICE) in the average of 12 %.

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Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

Cited by 10 publications

References 20 publications

Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

Denoising Autoencoder-Based Missing Value Imputation for Smart Meters

Fusion of energy sensors with missing values

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