A novel approach based on a feature selection procedure for residential load identification

Doğan, Rasim

doi:10.1016/j.segan.2021.100488

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Since the events are detected, only an interval of the acquired signals around the event is processed afterwards to identify the load (or appliance). The load identification can be implemented by means of different approaches, such as a Bayesian approach, principal component analysis (PCA) [ 10 ], clustering, SVM [ 23 ], or deep neural networks [ 15 , 16 , 37 ], which have become more relevant recently.…”

Section: Global Architecture Overviewmentioning

confidence: 99%

“…In general terms, NILM techniques are often based on the detection of any change in current or power signals [ 13 ], where features are extracted [ 14 , 15 ] to identify loads by applying different classification methods [ 7 , 16 , 17 ]. From that point, human activity can be inferred based on the usage pattern of some appliances, as they are strongly related to certain daily activities [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Anomalies in Daily Activities Using Data from Smart Meters

Hernández,

Nieto,

de Diego-Otón

et al. 2024

Sensors

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The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy consumption has also been dedicated to the areas of social work and health. In this context, smart meters are considered single-point non-intrusive sensors that might be used to monitor the behaviour and activity patterns of people living in a household. This work describes the design of a short-term behavioural alarm generator based on the processing of energy consumption data coming from a commercial smart meter. The device captured data from a household for a period of six months, thus providing the consumption disaggregated per appliance at an interval of one hour. These data were used to train different intelligent systems, capable of estimating the predicted consumption for the next one-hour interval. Four different approaches have been considered and compared when designing the prediction system: a recurrent neural network, a convolutional neural network, a random forest, and a decision tree. By statistically analysing these predictions and the actual final energy consumption measurements, anomalies can be detected in the undertaking of three different daily activities: sleeping, breakfast, and lunch. The recurrent neural network achieves an F1-score of 0.8 in the detection of these anomalies for the household under analysis, outperforming other approaches. The proposal might be applied to the generation of a short-term alarm, which can be involved in future deployments and developments in the field of ambient assisted living.

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Section: Global Architecture Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Anomalies in Daily Activities Using Data from Smart Meters

Hernández,

Nieto,

de Diego-Otón

et al. 2024

Sensors

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“…Additionally, it does not provide the overall duration of operation in different modes over specific periods, as this method solely relies on power consumption data. Akarslan et al [20] made a significant contribution by addressing the challenge of selecting features and classifiers, especially considering the high computational complexity of ML techniques, which deteriorates with an increasing number of inputs. In their approach, they integrated the ReliefF feature selection method with classifiers for load identification.…”

Section: Related Work On Load Forecastingmentioning

confidence: 99%

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Zaboli,

Kasimalla,

Park

et al. 2024

Energies

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Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

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“…Recently, a novel approach based on a feature selection procedure has been introduced for residential load identification [27]. The reported approach uses a feature set comprises of first five odd harmonic currents, total harmonic distortion (THD) and angle with the backend classifiers, radial basis function (RBF) and Elman neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…The reported approach uses a feature set comprises of first five odd harmonic currents, total harmonic distortion (THD) and angle with the backend classifiers, radial basis function (RBF) and Elman neural networks. It is reported that Elman neural networks outperforms the RBF classifier with the use of fewer data samples for load identification [27]. Hu et al proposed a wavelet decomposition based standard deviation multiple method for event detection under complex startup process in smart homes [28].…”

Section: Introductionmentioning

confidence: 99%