Proposing suitable data imputation methods by adopting a Stage wise approach for various classes of smart meters missing data – Practical approach

Hemanth, G. R.; Raja, S. Charles

doi:10.1016/j.eswa.2021.115911

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Data is the foundation of digital power grid regulation and big data asset management. However, there are some missing or abnormal data in the digital power Big data acquisition architecture of digital power grid regulation grid regulation big data obtained in reality [7]. There are two main reasons for this phenomenon: one is the error of reading table of terminal equipment, deviation of data transmission and other reasons leading to abnormal data; Second, even if the data collection structure is normal, it will also cause abnormal changes in the digital power grid regulation big data due to the impact of special events, weather changes, line maintenance power outage and other factors.…”

Section: Data Collectionmentioning

confidence: 99%

Model construction of big data asset management system for digital power grid regulation

Xu,

Zhang,

Duan

2023

ITC

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There are many and complex big data for digital power grid regulation, which makes it more difficult to manage big data assets. Therefore, a model of big data asset management system for digital power grid regulation has been built. The model consists of three parts: data collection, data security storage and data index. The data acquisition architecture is designed, and the grey prediction method is used to fill the missing values and correct the abnormal values of the data acquisition results. Store the filled and amended data in the blockchain to ensure data security. The AR-tree index organization is used to realize the digital grid regulation big data index and achieve the goal of high-quality management of digital grid regulation big data assets. The experimental results show that the average recall and precision of this method are 96.9% and 97.9% respectively, and the data collection quality is high; After the application of this method, there is almost no non security data, and the proportion of security data is higher, which shows that this method can ensure the security of big data storage; The response time of digital power grid regulation big data index is less than 0.21s, and the index efficiency is higher.

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Section: Data Collectionmentioning

confidence: 99%

Model construction of big data asset management system for digital power grid regulation

Xu,

Zhang,

Duan

2023

ITC

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“…Venkatraman et al [28] developed a model based on data collected by SMs that recover loads related to distribution networks. Hemanth et al [29] proposed a method to recover missing data from SM measurements by using a particle swarm optimization (PSO) algorithm. Al Khafaf et al [30] studied the impact of using electrical energy storage in residential environments based on the use of SMs.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time

2021

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Nowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.

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“…A fuzzy inductive reasoning method was discussed to deal with the missing data during the forecasting process in smart grids [ 20 ]. A six-stage particle swarm optimization imputation method was implemented for smart meter data collected from an Indian institution [ 21 ]. An imputation method based on a denoising autoencoder was presented in [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Prakash

Kumar

Moganti

et al. 2022

Sensors

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Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.

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Proposing suitable data imputation methods by adopting a Stage wise approach for various classes of smart meters missing data – Practical approach

Cited by 8 publications

References 20 publications

Model construction of big data asset management system for digital power grid regulation

Model construction of big data asset management system for digital power grid regulation

Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

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