Detection of Anomalies in Household Appliances from Disaggregated Load Consumption

Castangia, Marco; Sappa, Riccardo; Girmay, Awet Abraha; Camarda, Christian; Macii, Enrico; Patti, Edoardo

doi:10.1109/sest50973.2021.9543232

Cited by 4 publications

(4 citation statements)

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“…Among the key applications of anomaly detection by load monitoring, are forecasting maintenance and energy efficiency [12]. Thus, a smart plug, smart appliance, and other appliance-level monitoring devices are needed to continuously monitor the power consumption of individual appliances in a house [8]. However, identifying anomalies, and their nature of them should also be considered, which can be categorized, based on different dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, it facilitates the prediction of end-users power demand as well as performing an optimal energy distribution by grid operators depending on specific end-users' needs. In addition, electrical anomalies are less likely to remain unnoticed for a long period of time which would result in higher power consumption or damage in the most critical cases [8].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, for optimization purposes in smart homes, via implementing load monitoring systems and formulating smart anomaly detection models using machine learning techniques, the abnormality can be mitigated [9]. To do so, it is essential to analyze the energy consumption of households in order to identify consumption patterns and extract valuable information from smart homes [8] [10].…”

Section: Introductionmentioning

confidence: 99%

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Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach

Zamani¹,

Talebi²,

Stevens³

2023

Preprint

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Fixing energy leakage caused by different anomalies can result in significant energy savings and extended appliance life. Further, it assists grid operators in scheduling their resources to meet the actual needs of end users, while helping end users reduce their energy costs. In this paper, we analyze the patterns pertaining to the power consumption of dishwashers used in two houses of the REFIT dataset. Then two autoencoder (AEs) with 1D-CNN and TCN as backbones are trained to differentiate the normal patterns from the abnormal ones. Our results indicate that TCN outperforms CNN1D in detecting anomalies in energy consumption. Finally, the data from the Fridge_Freezer and the Freezer of house No. 3 in REFIT is also used to evaluate our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach

Zamani¹,

Talebi²,

Stevens³

2023

Preprint

View full text Add to dashboard Cite

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“…The authors were able to predict the maximum level voltage using historical fault data and allocating the location of fault occurrence with the help of the proposed model. Meanwhile, Castangia et al (2021) employed an anomaly detection framework to track the hourly energy usage of three common power absorption sources: the baseline, the refrigerator, and electrical gadgets. As the focus of their research, they concentrated on single-point deviations and aberrant patterns.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques

Elhadad¹,

Tan²,

Tan³

2022

IJTech

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Electricity demand is increasing proportionally to the increase in power usage. Without a doubt, energy efficiency has gained significant importance and attention, with one of the primary concerns being the detection and forecasting of abnormal consumption. In this paper, the authors proposed a method to predict the occurrence of abnormal consumption behavior in advance. The proposed method utilizes the Isolation Forest algorithm to label the smart meter electricity consumption readings as normal or abnormal. It generates a sequence of data with varying lengths. Based on the data sequence, two supervised machine learning algorithms, Random Forest, and Decision Tree were developed to forecast the occurrence of power anomaly consumption. Experiment results showed that the proposed methods consistently detect and predict the abnormal status 30 minutes ahead. There is no significant difference between Random Forest and Decision Tree performance on different smart meter readings, dataset sizes, and other data sequence lengths. The proposed methods portray an alternative approach that is capable of autolabel normal and abnormal data and, as a result, dealing with the sequence of label data in the prediction process while avoiding the dynamic behavior of the power consumption data.

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