Low Complexity Non-Intrusive Load Disaggregation of Air Conditioning Unit and Electric Vehicle Charging

Rehman, Attique Ur; Lie, Tek Tjing; Vallès, Brice; Tito, Shafiqur Rahman

doi:10.1109/isgt-asia.2019.8881113

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…1 is primarily targeting the non-intrusive load inference of water heating (WH) load element at the circuit-level configuration. However, this methodology is also viable for the non-intrusive load inference of other load elements, even at the appliance-level configuration [14]. WH circuit is selected due to the attributes of the employed practical load database: data granularity and availability of the circuits.…”

Section: Methodsmentioning

confidence: 99%

“…A is another performance metric used for the evaluation of classification models and is defined as the prediction fraction the model classifies correctly [57], given as in (14).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…To identify individual loads based on the extracted features, numerous artificial-intelligence-based techniques are adopted by the research community. In this context, machine learning (ML) is widely employed, such as the k-nearest neighbors (k-NN) model, which is successfully deployed to disaggregate the air conditioning unit and electric vehicle charging [14]. Likewise, the disaggregation of an air condi-tioning unit is also carried out using a support vector machine (SVM) in [15].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Lie

Vallès³

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation. Non-intrusive load monitoring (NILM) offers many promising applications in the context of energy efficiency and conservation. Load classification is a key component of NILM that relies on different artificial intelligence techniques, e.g., machine learning. This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis. Moreover, this study also analyzes the role of input feature space dimensionality in the context of classification performance. For the above purposes, an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis, it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data. The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier. Furthermore, it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

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Section: Methodsmentioning

confidence: 99%

“…A is another performance metric used for the evaluation of classification models and is defined as the prediction fraction the model classifies correctly [57], given as in (14).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Lie

Vallès³

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…Using precision, recall and F1-score to evaluate the method, an efficacy of 86.05% was achieved. Additionally, a supervised event NILM method was developed by A. Rheman, T. Lie and S. Tio in [8]. Air conditioning and EV connection and disconnections were classified from segregated measurements using low sampling rate data.…”

Section: Work To Datementioning

confidence: 99%

Supervised Non-Intrusive Load Monitoring Algorithm for Electric Vehicle Identification

Jaramillo

Laverty

Rincón

et al. 2020

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Transport sector electrification represents an increase in the number of electric vehicles (EV), producing significant variations in the distribution network dynamics. As a result, bidirectional power flow, overload and load unbalances are caused at the low voltage level due to unexpected increased load peaks. Non-intrusive load monitoring (NILM) methods have been developed as a strategy for energy management systems, applied to the customer side producing energy savings. This research presents a NILM methodology based on a low complexity conventional supervised machine learning pipeline. Our approach uses Principal Component Analysis (PCA) and Random Forest (RF) to detect the presence of a charging electric vehicle on the electricity network. By processing low sampling rate active power data, this approach provides a simple but feasible method that can be applied to smart meters. This provides useful data analysis for distribution network operators (DNO) to effectively deal with variability caused by these low carbon loads in the distribution grid. Achieving an overall efficacy of 92.68%, the proposed method can be compared with other state of the art methods developed under higher complexity techniques.

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“…Recent advancements in computational capabilities significantly aided the NILM classification methodologies. In this context, numerous techniques are adopted by the research community for the NILM process, which include but are not limited to dynamic time wrapping [28,30], optimization [12,31], machine learning [32][33][34][35][36], neural networks [25,37], and deep learning [38,39]. However, in the context of NILM, supervised machine-learning models are more frequently used as compared to other methodologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Rehman

Lie

Vallès³

et al. 2020

Inventions

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The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

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Low Complexity Non-Intrusive Load Disaggregation of Air Conditioning Unit and Electric Vehicle Charging

Cited by 18 publications

References 17 publications

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Supervised Non-Intrusive Load Monitoring Algorithm for Electric Vehicle Identification

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

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