A non-intrusive appliance load monitoring for efficient energy consumption based on Naive Bayes classifier

Yang, Chuan; Soh, Chit Siang; Yap, Vooi Voon

doi:10.1016/j.suscom.2017.03.001

Cited by 26 publications

(15 citation statements)

References 23 publications

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“…The NB classifier assumes that the values of the features are conditionally independent given the value of class variable. With NB algorithm, the training dataset from experiments predicts the class information and identifies the classification for which the classification membership is not available [17]. The process has 4 steps:…”

Section: Naïve Bayesmentioning

confidence: 99%

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

Depren

2019

Spor Ve Performans Araştırmaları Dergisi

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The main purpose of this study is to determine which factors have an important role in National Basketball Association (NBA) players' shooting accuracy. To achieve this purpose, player-based raw-dataset for each match on the 2014-2015 NBA season is used in this study. Seven different machine learning algorithms are applied and also 10-fold cross-validation with 10-repeat process is performed to avoid the overfitting problem. Nine independent variables and one binary dependent variable are included in the analysis. According to the results of the analysis, k-nearest neighbor algorithm is the best machine learning algorithm among other algorithms that are used in the analysis in order to predict whether basketball player can make a shot or not. Shot Distance, distance of closest defense player and touch time are identified as the most important factors affecting player's successful field goal accuracy. Since the successful field goal performance is very influential in winning the game, the results of this study can be used as a guide for training programs to basketball players and team coaches.

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Section: Naïve Bayesmentioning

confidence: 99%

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

Depren

2019

Spor Ve Performans Araştırmaları Dergisi

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“…where PC i and RC i are the recall and precision for appliance i. PC i and RC i are described in Equation (11).    Based on above description, F-score ranges from 0 (0% accuracy in state detection) to 1 (100% accuracy in state detection).…”

Section: Performance Metricsmentioning

confidence: 99%

“…Hart proposed event-based method to identify appliances [9]. Literature [11] adopts Naive Bayes classifier to identify the multiple appliances operating. Wavelet transform are used to identify the appliances by analyzing the power spectrum [12].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern

Wang

Yang

2018

Applied Sciences

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Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm's computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.

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“…Since low-frequency signatures are readily available from smart meters, the load disaggregation methods using low-frequency features are an essential development trend of NILM. Many studies use low-frequency features and have achieved excellent results [15][16][17][18][19][20]. In the literature [15,16,20], hidden Markov model-based methods are used to decompose low-frequency total powers.…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, the most significant contribution appliances are iteratively decomposed, but the previous results will affect the identification of low-power appliances. In [18], naïve Bayesian estimation is used to classify and identify the combination of the active power of different appliances; the results show that the method has a limit in identifying the appliances with similar power, and that the power variation of the appliances during operation is not considered. In [19], a current with little change during appliance operation is chosen as the load signature, and the differential evolution algorithm is used for decomposition.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

et al. 2019

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In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.

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A non-intrusive appliance load monitoring for efficient energy consumption based on Naive Bayes classifier

Cited by 26 publications

References 23 publications

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

The Effectiveness of Different Machine Learning Algorithms on Basketball Players’ Shooting Performance

An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern

An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

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