Identification and characterization of irregular consumptions of load data

Sharma, Desh Deepak; Singh, S. N.; Lin, Jeremy; Foruzan, Elham

doi:10.1007/s40565-017-0268-1

Cited by 11 publications

(2 citation statements)

References 38 publications

(86 reference statements)

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“…Outlier detection using k NN and ISWP is done for the above three cases, and the performance is compared in Figure 3. It is evident from the figure that the application of the k NN‐based approach 10,27 on volatile time‐series like PV generation, results in the detection of a large number of false outliers along with true outliers. This is because the method is incapable of detecting an outlier at a particular time‐instant; instead, it marks many data points near an outlier 3 .…”

Section: Results Analysesmentioning

confidence: 99%

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

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Steady-state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction-based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction-based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time-steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor and k-nearest neighbor approach is presented. Later, the improved out-of-sample forecasting accuracy canonizes the proposed method's concert compared to both the above techniques and the case without preprocessing.

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Section: Results Analysesmentioning

confidence: 99%

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

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“…Sharma et al [16] have used the concept of local outlier factor (LOF) in density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to identify unusual load patterns in two datasets from USA and India. LOF is the ratio of density of a data point to the density of its k-nearest neighbors.…”

Section: A: Unsupervised Learningmentioning

confidence: 99%

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

et al. 2020

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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

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Methods, data sources and applications of the Artificial Intelligence in the Energy Poverty context: A review

López-Vargas

Ledezma-Espino

Sanchis-de-Miguel

2022

Energy and Buildings

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Identification and characterization of irregular consumptions of load data

Cited by 11 publications

References 38 publications

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Methods, data sources and applications of the Artificial Intelligence in the Energy Poverty context: A review

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